Skip to content

CAnDOIT

This module provides the CausalDiscoveryMethod class.

Classes

CausalDiscoveryMethod: abstract class used by all the causal discovery algorithms.

CausalDiscoveryMethod

Bases: ABC

CausalDiscoveryMethod class.

CausalDiscoveryMethod is an abstract causal discovery method for large-scale time series datasets.

Source code in causalflow/causal_discovery/CausalDiscoveryMethod.py 
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
class CausalDiscoveryMethod(ABC):
    """
    CausalDiscoveryMethod class.

    CausalDiscoveryMethod is an abstract causal discovery method for 
    large-scale time series datasets.
    """

    def __init__(self, 
                 data: Data, 
                 min_lag, max_lag, 
                 verbosity: CPLevel, 
                 alpha = 0.05, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class contructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            verbosity (CPLevel): verbosity level.
            alpha (float, optional): significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.

        """
        self.data = data
        self.alpha = alpha
        self.min_lag = min_lag
        self.max_lag = max_lag
        self.CM = DAG(self.data.features, min_lag, max_lag, neglect_only_autodep)
        self.neglect_only_autodep = neglect_only_autodep

        self.resfolder = resfolder
        self.respath, self.dag_path, self.ts_dag_path = None, None, None
        if resfolder is not None:
            logpath, self.respath, self.dag_path, self.ts_dag_path = utils.get_selectorpath(resfolder)  
            self.logger = Logger(logpath, clean_cls)
            sys.stdout = self.logger

        CP.set_verbosity(verbosity)


    @abstractmethod
    def run(self) -> DAG:
        """
        Run causal discovery method.

        Returns:
            DAG: causal model.
        """
        pass


    def load(self, res_path):
        """
        Load previously estimated result .

        Args:
            res_path (str): pickle file path.
        """
        with open(res_path, 'rb') as f:
            r = pickle.load(f)
            self.CM = r['causal_model']
            self.alpha = r['alpha']
            self.dag_path = r['dag_path']
            self.ts_dag_path = r['ts_dag_path']


    def save(self):
        """Save causal discovery result as pickle file if resfolder is set."""
        if self.respath is not None:
            if self.CM:
                res = dict()
                res['causal_model'] = copy.deepcopy(self.CM)
                res['alpha'] = self.alpha
                res['dag_path'] = self.dag_path
                res['ts_dag_path'] = self.ts_dag_path
                with open(self.respath, 'wb') as resfile:
                    pickle.dump(res, resfile)
            else:
                CP.warning("Causal model impossible to save")

__init__(data, min_lag, max_lag, verbosity, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class contructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
verbosity CPLevel

verbosity level.

 required
alpha float

significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/CausalDiscoveryMethod.py 
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
def __init__(self, 
             data: Data, 
             min_lag, max_lag, 
             verbosity: CPLevel, 
             alpha = 0.05, 
             resfolder = None,
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class contructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        verbosity (CPLevel): verbosity level.
        alpha (float, optional): significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.

    """
    self.data = data
    self.alpha = alpha
    self.min_lag = min_lag
    self.max_lag = max_lag
    self.CM = DAG(self.data.features, min_lag, max_lag, neglect_only_autodep)
    self.neglect_only_autodep = neglect_only_autodep

    self.resfolder = resfolder
    self.respath, self.dag_path, self.ts_dag_path = None, None, None
    if resfolder is not None:
        logpath, self.respath, self.dag_path, self.ts_dag_path = utils.get_selectorpath(resfolder)  
        self.logger = Logger(logpath, clean_cls)
        sys.stdout = self.logger

    CP.set_verbosity(verbosity)

load(res_path)

Load previously estimated result .

Parameters:

Name Type Description Default
res_path str

pickle file path.

 required
Source code in causalflow/causal_discovery/CausalDiscoveryMethod.py 
78
79
80
81
82
83
84
85
86
87
88
89
90
def load(self, res_path):
    """
    Load previously estimated result .

    Args:
        res_path (str): pickle file path.
    """
    with open(res_path, 'rb') as f:
        r = pickle.load(f)
        self.CM = r['causal_model']
        self.alpha = r['alpha']
        self.dag_path = r['dag_path']
        self.ts_dag_path = r['ts_dag_path']

run() abstractmethod

Run causal discovery method.

Returns:

Name Type Description
DAG DAG

causal model.
Source code in causalflow/causal_discovery/CausalDiscoveryMethod.py 
67
68
69
70
71
72
73
74
75
@abstractmethod
def run(self) -> DAG:
    """
    Run causal discovery method.

    Returns:
        DAG: causal model.
    """
    pass

save()

Save causal discovery result as pickle file if resfolder is set.

Source code in causalflow/causal_discovery/CausalDiscoveryMethod.py 
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
def save(self):
    """Save causal discovery result as pickle file if resfolder is set."""
    if self.respath is not None:
        if self.CM:
            res = dict()
            res['causal_model'] = copy.deepcopy(self.CM)
            res['alpha'] = self.alpha
            res['dag_path'] = self.dag_path
            res['ts_dag_path'] = self.ts_dag_path
            with open(self.respath, 'wb') as resfile:
                pickle.dump(res, resfile)
        else:
            CP.warning("Causal model impossible to save")

This module provides the FPCMCI class.

Classes

FPCMCI: class containing the FPCMCI causal discovery algorithm.

FPCMCI

Bases: CausalDiscoveryMethod

F-PCMCI causal discovery method.

Source code in causalflow/causal_discovery/FPCMCI.py 
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
class FPCMCI(CausalDiscoveryMethod):
    """F-PCMCI causal discovery method."""

    def __init__(self, 
                 data: Data, 
                 min_lag, max_lag, 
                 sel_method: SelectionMethod, val_condtest: CondIndTest, 
                 verbosity: CPLevel, 
                 f_alpha = 0.05, 
                 alpha = 0.05, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class contructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            sel_method (SelectionMethod): selection method.
            val_condtest (CondIndTest): validation method.
            verbosity (CPLevel): verbosity level.
            f_alpha (float, optional): filter significance level. Defaults to 0.05.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

        self.f_alpha = f_alpha
        self.sel_method = sel_method

        self.validator = myPCMCI(self.alpha, min_lag, max_lag, val_condtest, verbosity, neglect_only_autodep = neglect_only_autodep)       


    def run_filter(self):
        """Run filter method."""
        CP.info("\n")
        CP.info(DASH)
        CP.info("Selecting relevant features among: " + str(self.data.features))
        CP.info("Selection method: " + self.sel_method.name)
        CP.info("Significance level: " + str(self.f_alpha))
        CP.info("Max lag time: " + str(self.max_lag))
        CP.info("Min lag time: " + str(self.min_lag))
        CP.info("Data length: " + str(self.data.T))

        self.sel_method.initialise(self.data, self.f_alpha, self.min_lag, self.max_lag, self.CM)
        self.CM = self.sel_method.compute_dependencies()  


    def run(self, remove_unneeded = True, nofilter = False) -> DAG:
        """
        Run F-PCMCI.

        Args:
            remove_unneeded (bool, optional): Bit to remove unneeded (isolated) variables. Defaults to True.
            nofilter (bool, optional): Bit to run F-PCMCI without filter. Defaults to False.

        Returns:
            DAG: causal model.
        """
        link_assumptions = None

        if not nofilter:
            ## 1. FILTER
            self.run_filter()

            # list of selected features based on filter dependencies
            self.CM.remove_unneeded_features()
            if not self.CM.features: return None, None

            ## 2. VALIDATOR
            # shrink dataframe d by using the filter result
            self.data.shrink(self.CM.features)

            # selected links to check by the validator
            link_assumptions = self.CM.get_link_assumptions()

            # calculate dependencies on selected links
            f_dag = copy.deepcopy(self.CM)

        if self.min_lag != 0:
            self.CM = self.validator.run(self.data, link_assumptions)
        else:
            self.CM = self.validator.run_plus(self.data, link_assumptions)

        # list of selected features based on validator dependencies
        if remove_unneeded: self.CM.remove_unneeded_features()

        # Saving final causal model
        if not nofilter: self._print_differences(f_dag, self.CM)
        self.save()

        if self.resfolder is not None: self.logger.close()
        return self.CM


    def load(self, res_path):
        """
        Load previously estimated result.

        Args:
            res_path (str): pickle file path.
        """
        with open(res_path, 'rb') as f:
            r = pickle.load(f)
            self.CM = r['causal_model']
            self.f_alpha = r['filter_alpha']
            self.alpha = r['alpha']
            self.dag_path = r['dag_path']
            self.ts_dag_path = r['ts_dag_path']


    def save(self):
        """Save causal discovery result as pickle file if resfolder is set."""
        if self.respath is not None:
            if self.CM:
                res = dict()
                res['causal_model'] = copy.deepcopy(self.CM)
                res['features'] = copy.deepcopy(self.CM.features)
                res['filter_alpha'] = self.f_alpha
                res['alpha'] = self.alpha
                res['dag_path'] = self.dag_path
                res['ts_dag_path'] = self.ts_dag_path
                with open(self.respath, 'wb') as resfile:
                    pickle.dump(res, resfile)
            else:
                CP.warning("Causal model impossible to save")


    def _print_differences(self, old_dag : DAG, new_dag : DAG):
        """
        Print difference between old and new dependencies.

        Args:
            old_dep (DAG): old dag.
            new_dep (DAG): new dag.
        """
        # Check difference(s) between validator and filter dependencies
        list_diffs = list()
        tmp = copy.deepcopy(old_dag)
        for t in tmp.g:
            if t not in new_dag.g:
                list_diffs.append(t)
                continue

            for s in tmp.g[t].sources:
                if s not in new_dag.g[t].sources:
                    list_diffs.append((s[0], s[1], t))

        if list_diffs:
            CP.info("\n")
            CP.info(DASH)
            CP.info("Difference(s):")
            for diff in list_diffs: 
                if type(diff) is tuple:
                    CP.info("Removed (" + str(diff[0]) + " -" + str(diff[1]) +") --> (" + str(diff[2]) + ")")
                else:
                    CP.info(diff + " removed")

__init__(data, min_lag, max_lag, sel_method, val_condtest, verbosity, f_alpha=0.05, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class contructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
sel_method SelectionMethod

selection method.

 required
val_condtest CondIndTest

validation method.

 required
verbosity CPLevel

verbosity level.

 required
f_alpha float

filter significance level. Defaults to 0.05.

 0.05
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/FPCMCI.py 
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
def __init__(self, 
             data: Data, 
             min_lag, max_lag, 
             sel_method: SelectionMethod, val_condtest: CondIndTest, 
             verbosity: CPLevel, 
             f_alpha = 0.05, 
             alpha = 0.05, 
             resfolder = None,
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class contructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        sel_method (SelectionMethod): selection method.
        val_condtest (CondIndTest): validation method.
        verbosity (CPLevel): verbosity level.
        f_alpha (float, optional): filter significance level. Defaults to 0.05.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

    self.f_alpha = f_alpha
    self.sel_method = sel_method

    self.validator = myPCMCI(self.alpha, min_lag, max_lag, val_condtest, verbosity, neglect_only_autodep = neglect_only_autodep)

load(res_path)

Load previously estimated result.

Parameters:

Name Type Description Default
res_path str

pickle file path.

 required
Source code in causalflow/causal_discovery/FPCMCI.py 
119
120
121
122
123
124
125
126
127
128
129
130
131
132
def load(self, res_path):
    """
    Load previously estimated result.

    Args:
        res_path (str): pickle file path.
    """
    with open(res_path, 'rb') as f:
        r = pickle.load(f)
        self.CM = r['causal_model']
        self.f_alpha = r['filter_alpha']
        self.alpha = r['alpha']
        self.dag_path = r['dag_path']
        self.ts_dag_path = r['ts_dag_path']

run(remove_unneeded=True, nofilter=False)

Run F-PCMCI.

Parameters:

Name Type Description Default
remove_unneeded bool

Bit to remove unneeded (isolated) variables. Defaults to True.

 True
nofilter bool

Bit to run F-PCMCI without filter. Defaults to False.

 False

Returns:

Name Type Description
DAG DAG

causal model.
Source code in causalflow/causal_discovery/FPCMCI.py 
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
def run(self, remove_unneeded = True, nofilter = False) -> DAG:
    """
    Run F-PCMCI.

    Args:
        remove_unneeded (bool, optional): Bit to remove unneeded (isolated) variables. Defaults to True.
        nofilter (bool, optional): Bit to run F-PCMCI without filter. Defaults to False.

    Returns:
        DAG: causal model.
    """
    link_assumptions = None

    if not nofilter:
        ## 1. FILTER
        self.run_filter()

        # list of selected features based on filter dependencies
        self.CM.remove_unneeded_features()
        if not self.CM.features: return None, None

        ## 2. VALIDATOR
        # shrink dataframe d by using the filter result
        self.data.shrink(self.CM.features)

        # selected links to check by the validator
        link_assumptions = self.CM.get_link_assumptions()

        # calculate dependencies on selected links
        f_dag = copy.deepcopy(self.CM)

    if self.min_lag != 0:
        self.CM = self.validator.run(self.data, link_assumptions)
    else:
        self.CM = self.validator.run_plus(self.data, link_assumptions)

    # list of selected features based on validator dependencies
    if remove_unneeded: self.CM.remove_unneeded_features()

    # Saving final causal model
    if not nofilter: self._print_differences(f_dag, self.CM)
    self.save()

    if self.resfolder is not None: self.logger.close()
    return self.CM

run_filter()

Run filter method.

Source code in causalflow/causal_discovery/FPCMCI.py 
57
58
59
60
61
62
63
64
65
66
67
68
69
def run_filter(self):
    """Run filter method."""
    CP.info("\n")
    CP.info(DASH)
    CP.info("Selecting relevant features among: " + str(self.data.features))
    CP.info("Selection method: " + self.sel_method.name)
    CP.info("Significance level: " + str(self.f_alpha))
    CP.info("Max lag time: " + str(self.max_lag))
    CP.info("Min lag time: " + str(self.min_lag))
    CP.info("Data length: " + str(self.data.T))

    self.sel_method.initialise(self.data, self.f_alpha, self.min_lag, self.max_lag, self.CM)
    self.CM = self.sel_method.compute_dependencies()

save()

Save causal discovery result as pickle file if resfolder is set.

Source code in causalflow/causal_discovery/FPCMCI.py 
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
def save(self):
    """Save causal discovery result as pickle file if resfolder is set."""
    if self.respath is not None:
        if self.CM:
            res = dict()
            res['causal_model'] = copy.deepcopy(self.CM)
            res['features'] = copy.deepcopy(self.CM.features)
            res['filter_alpha'] = self.f_alpha
            res['alpha'] = self.alpha
            res['dag_path'] = self.dag_path
            res['ts_dag_path'] = self.ts_dag_path
            with open(self.respath, 'wb') as resfile:
                pickle.dump(res, resfile)
        else:
            CP.warning("Causal model impossible to save")

This module provides the CAnDOIT class.

Classes

CAnDOIT: class containing the CAnDOIT causal discovery algorithm.

CAnDOIT

Bases: CausalDiscoveryMethod

CAnDOIT causal discovery method.

Source code in causalflow/causal_discovery/CAnDOIT.py 
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
class CAnDOIT(CausalDiscoveryMethod):
    """CAnDOIT causal discovery method."""

    def __init__(self, 
                 observation_data: Data, 
                 intervention_data: dict, 
                 max_lag,
                 sel_method: SelectionMethod, val_condtest: CondIndTest, 
                 verbosity: CPLevel, 
                 f_alpha = 0.05, 
                 alpha = 0.05, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 exclude_context = True,
                 plot_data = False,
                 clean_cls = True):
        """
        Class contructor.

        Args:
            observation_data (Data): observational data to analyse.
            intervention_data (dict): interventional data to analyse in the form {INTERVENTION_VARIABLE : Data (same variables of observation_data)}.
            max_lag (int): maximum time lag.
            sel_method (SelectionMethod): selection method.
            val_condtest (CondIndTest): validation method.
            verbosity (CPLevel): verbosity level.
            f_alpha (float, optional): filter significance level. Defaults to 0.05.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            exclude_context (bool, optional): Bit for neglecting context variables. Defaults to False.
            plot_data (bool, optional): Bit for plotting your data. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        min_lag = 0

        self.obs_data = observation_data
        self.systems = observation_data.features
        self.contexts = []
        self.sys_context = {}
        for k in intervention_data.keys():
            self.contexts.append("C" + k)
            self.sys_context[k] = "C" + k
        self.vars = self.systems + self.contexts

        self.f_alpha = f_alpha
        self.sel_method = sel_method
        self.val_condtest = val_condtest
        self.exclude_context = exclude_context
        super().__init__(self.obs_data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

        # Create filter and validator data
        self.filter_data, self.validator_data = self._prepare_data(self.obs_data, intervention_data, plot_data)


        CP.info("\n")
        CP.info(DASH)
        CP.info("Observational data length: " + str(observation_data.T))
        CP.info("Interventional data length: " + str(sum([d.T for d in intervention_data.values()])))
        CP.info("Min lag time: " + str(min_lag))
        CP.info("Max lag time: " + str(max_lag))
        CP.info("Filter significance level: " + str(f_alpha))
        CP.info("PCMCI significance level: " + str(alpha))
        CP.info("Selection method: " + sel_method.name)


    @property    
    def isThereInterv(self) -> bool:
        """
        Check whether an intervention is present or not.

        Returns:
            bool: flag to identify if an intervention is present or not.
        """
        return len(list(self.sys_context.keys())) > 0


    def JCI_assumptions(self):
        """Initialise the algorithm initial causal structure with the JCI assumptions."""
        # ! JCI Assmpution 1: No system variable causes any context variable
        # ! JCI Assmpution 2: No context variable is confounded with a system variable
        # ! JCI Assmpution 3: The context distribution contains no (conditional) independences

        knowledge = {self.vars.index(f): dict() for f in self.vars}

        # ! JCI Assmpution 1
        for k in self.contexts:
            for x in self.systems:
                for tau_i in range(0, self.max_lag + 1):
                    knowledge[self.vars.index(k)][(self.vars.index(x), -tau_i)] = ''

        # ! JCI Assmpution 2
        for k in self.contexts:
            for x in self.systems:
                if x not in self.sys_context or (x in self.sys_context and k != self.sys_context[x]):
                    for tau_i in range(0, self.max_lag + 1): knowledge[self.vars.index(x)][(self.vars.index(k), -tau_i)] = ''
                elif x in self.sys_context and k == self.sys_context[x]:
                    knowledge[self.vars.index(x)][(self.vars.index(k), 0)] = '-->'
                    knowledge[self.vars.index(k)][(self.vars.index(x), 0)] = '<--'
                    for tau_i in range(1, self.max_lag + 1): knowledge[self.vars.index(x)][(self.vars.index(k), -tau_i)] = ''
                    for tau_i in range(1, self.max_lag + 1): knowledge[self.vars.index(k)][(self.vars.index(x), -tau_i)] = ''

        # ! JCI Assmpution 3
        for k1 in self.contexts:
            for k2 in remove_from_list(self.contexts, k1):
                knowledge[self.vars.index(k1)][(self.vars.index(k2), 0)] = '<->'
                # for tau_i in range(0, self.max_lag + 1): knowledge[self.vars.index(k1)][(self.vars.index(k2), -tau_i)] = '<->'

        # ! This models the context variables as chain across different time steps
        for k in self.contexts:
            for tau_i in range(1, self.max_lag + 1):
                knowledge[self.vars.index(k)][(self.vars.index(k), -tau_i)] = '-->' if tau_i == 1 else ''




        out = {}
        for j in range(len(self.vars)):
            inner_dict = {} 

            for i in range(len(self.vars)):
                for tau_i in range(0, self.max_lag + 1):
                    if tau_i > 0 or i != j:
                        value = "o?>" if tau_i > 0 else "o?o"
                        inner_dict[(i, -tau_i)] = value

            out[j] = inner_dict

        for j, links_j in knowledge.items():
            for (i, lag_i), link_ij in links_j.items():
                if link_ij == "":
                    del out[j][(i, lag_i)]
                else: 
                    out[j][(i, lag_i)] = link_ij
        return out


    def run_filter(self):
        """Run filter method."""
        CP.info("Selecting relevant features among: " + str(self.filter_data.features))

        self.sel_method.initialise(self.obs_data, self.f_alpha, self.min_lag, self.max_lag, self.CM)
        self.CM = self.sel_method.compute_dependencies()


    def run_validator(self, link_assumptions = None) -> DAG:
        """
        Run Validator (LPCMCI).

        Args:
            link_assumptions (dict, optional): link assumption with context. Defaults to None.

        Returns:
            DAG: causal model with context.
        """
        self.validator = myLPCMCI(self.validator_data,
                                self.max_lag,
                                self.sys_context,
                                self.val_condtest,
                                CP.verbosity,
                                self.alpha)
        causal_model = self.validator.run(link_assumptions)
        causal_model.sys_context = self.CM.sys_context      

        return causal_model


    def run(self, remove_unneeded = True, nofilter = True) -> DAG:
        """
        Run CAnDOIT.

        Args:
            remove_unneeded (bool, optional): Bit to remove unneeded (isolated) variables. Defaults to True.
            nofilter (bool, optional): Bit to run CAnDOIT without filter. Defaults to False.

        Returns:
            DAG: causal model.
        """
        link_assumptions = None

        if not nofilter:
            #FIXME: to include also the filter. for now this is wrong
            ## 1. FILTER
            self.run_filter()

            # list of selected features based on filter dependencies
            self.CM.remove_unneeded_features()
            if not self.CM.features: return None, None

            self.obs_data.shrink(self.CM.features)
            f_dag = copy.deepcopy(self.CM)

            ## 2. VALIDATOR
            # Add dependencies corresponding to the context variables 
            # ONLY if the the related system variable is still present
            self.CM.add_context() 

            # shrink dataframe d by using the filter result
            self.validator_data.shrink(self.CM.features)

            # selected links to check by the validator
            link_assumptions = self.CM.get_link_assumptions()

        else:
            # fullg = DAG(self.validator_data.features, self.min_lag, self.max_lag, False)
            # fullg.sys_context = self.CM.sys_context
            link_assumptions = self.JCI_assumptions()

        # calculate dependencies on selected links
        self.CM = self.run_validator(link_assumptions)

        # list of selected features based on validator dependencies
        if remove_unneeded: self.CM.remove_unneeded_features()
        if self.exclude_context: self.CM.remove_context()

        self.save()

        return self.CM


    def load(self, res_path):
        """
        Load previously estimated result.

        Args:
            res_path (str): pickle file path.
        """
        with open(res_path, 'rb') as f:
            r = pickle.load(f)
            self.CM = r['causal_model']
            self.f_alpha = r['filter_alpha']
            self.alpha = r['alpha']
            self.dag_path = r['dag_path']
            self.ts_dag_path = r['ts_dag_path']


    def save(self):
        """Save causal discovery result as pickle file if resfolder is set."""
        if self.respath is not None:
            if self.CM:
                res = dict()
                res['causal_model'] = copy.deepcopy(self.CM)
                res['features'] = copy.deepcopy(self.CM.features)
                res['filter_alpha'] = self.f_alpha
                res['alpha'] = self.alpha
                res['dag_path'] = self.dag_path
                res['ts_dag_path'] = self.ts_dag_path
                with open(self.respath, 'wb') as resfile:
                    pickle.dump(res, resfile)
            else:
                CP.warning("Causal model impossible to save")


    def _prepare_data(self, obser_data, inter_data, plot_data):
        """
        Prepare data for filter and validator phases.

        Args:
            obser_data (Data): observational data.
            inter_data (Data): interventional data.
            plot_data (bool): boolean bit to plot the generated data.

        Returns:
            Data, Data: filter data obj and validator data obj.
        """
        # Filter phase data preparation
        filter_data = copy.deepcopy(obser_data.d)
        for int_data in inter_data.values(): filter_data = pd.concat([filter_data, int_data.d], axis = 0, ignore_index = True)
        filter_data = Data(filter_data, vars = obser_data.features)

        # Validator phase data preparation
        validator_data = copy.deepcopy(obser_data.d)
        context_vars = dict()
        for int_var, int_data in inter_data.items():

            # Create context variable name
            context_varname = 'C' + int_var

            # Store a dict of context variable and system variable corresponding to an intervention
            self.CM.sys_context[int_var] = context_varname

            # Create context variable data
            # context_data = np.ones(shape=int_data.d[int_var].shape)
            context_data = int_data.d[int_var]
            context_start = len(validator_data)
            context_end = context_start + len(context_data)
            context_vars[context_varname] = {'data': context_data, 'start': context_start, 'end': context_end}

            validator_data = pd.concat([validator_data, int_data.d], axis = 0, ignore_index = True)

        for var in context_vars:
            new_column = np.zeros(shape = (len(validator_data),))
            new_column[context_vars[var]['start']: context_vars[var]['end']] = context_vars[var]['data']
            validator_data[var] = new_column

        validator_data = Data(validator_data, vars = list(validator_data.columns))

        if plot_data: validator_data.plot_timeseries()
        return filter_data, validator_data

isThereInterv: bool property

Check whether an intervention is present or not.

Returns:

Name Type Description
bool bool

flag to identify if an intervention is present or not.

JCI_assumptions()

Initialise the algorithm initial causal structure with the JCI assumptions.

Source code in causalflow/causal_discovery/CAnDOIT.py 
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
def JCI_assumptions(self):
    """Initialise the algorithm initial causal structure with the JCI assumptions."""
    # ! JCI Assmpution 1: No system variable causes any context variable
    # ! JCI Assmpution 2: No context variable is confounded with a system variable
    # ! JCI Assmpution 3: The context distribution contains no (conditional) independences

    knowledge = {self.vars.index(f): dict() for f in self.vars}

    # ! JCI Assmpution 1
    for k in self.contexts:
        for x in self.systems:
            for tau_i in range(0, self.max_lag + 1):
                knowledge[self.vars.index(k)][(self.vars.index(x), -tau_i)] = ''

    # ! JCI Assmpution 2
    for k in self.contexts:
        for x in self.systems:
            if x not in self.sys_context or (x in self.sys_context and k != self.sys_context[x]):
                for tau_i in range(0, self.max_lag + 1): knowledge[self.vars.index(x)][(self.vars.index(k), -tau_i)] = ''
            elif x in self.sys_context and k == self.sys_context[x]:
                knowledge[self.vars.index(x)][(self.vars.index(k), 0)] = '-->'
                knowledge[self.vars.index(k)][(self.vars.index(x), 0)] = '<--'
                for tau_i in range(1, self.max_lag + 1): knowledge[self.vars.index(x)][(self.vars.index(k), -tau_i)] = ''
                for tau_i in range(1, self.max_lag + 1): knowledge[self.vars.index(k)][(self.vars.index(x), -tau_i)] = ''

    # ! JCI Assmpution 3
    for k1 in self.contexts:
        for k2 in remove_from_list(self.contexts, k1):
            knowledge[self.vars.index(k1)][(self.vars.index(k2), 0)] = '<->'
            # for tau_i in range(0, self.max_lag + 1): knowledge[self.vars.index(k1)][(self.vars.index(k2), -tau_i)] = '<->'

    # ! This models the context variables as chain across different time steps
    for k in self.contexts:
        for tau_i in range(1, self.max_lag + 1):
            knowledge[self.vars.index(k)][(self.vars.index(k), -tau_i)] = '-->' if tau_i == 1 else ''




    out = {}
    for j in range(len(self.vars)):
        inner_dict = {} 

        for i in range(len(self.vars)):
            for tau_i in range(0, self.max_lag + 1):
                if tau_i > 0 or i != j:
                    value = "o?>" if tau_i > 0 else "o?o"
                    inner_dict[(i, -tau_i)] = value

        out[j] = inner_dict

    for j, links_j in knowledge.items():
        for (i, lag_i), link_ij in links_j.items():
            if link_ij == "":
                del out[j][(i, lag_i)]
            else: 
                out[j][(i, lag_i)] = link_ij
    return out

__init__(observation_data, intervention_data, max_lag, sel_method, val_condtest, verbosity, f_alpha=0.05, alpha=0.05, resfolder=None, neglect_only_autodep=False, exclude_context=True, plot_data=False, clean_cls=True)

Class contructor.

Parameters:

Name Type Description Default
observation_data Data

observational data to analyse.

 required
intervention_data dict

interventional data to analyse in the form {INTERVENTION_VARIABLE : Data (same variables of observation_data)}.

 required
max_lag int

maximum time lag.

 required
sel_method SelectionMethod

selection method.

 required
val_condtest CondIndTest

validation method.

 required
verbosity CPLevel

verbosity level.

 required
f_alpha float

filter significance level. Defaults to 0.05.

 0.05
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
exclude_context bool

Bit for neglecting context variables. Defaults to False.

 True
plot_data bool

Bit for plotting your data. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/CAnDOIT.py 
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
def __init__(self, 
             observation_data: Data, 
             intervention_data: dict, 
             max_lag,
             sel_method: SelectionMethod, val_condtest: CondIndTest, 
             verbosity: CPLevel, 
             f_alpha = 0.05, 
             alpha = 0.05, 
             resfolder = None,
             neglect_only_autodep = False,
             exclude_context = True,
             plot_data = False,
             clean_cls = True):
    """
    Class contructor.

    Args:
        observation_data (Data): observational data to analyse.
        intervention_data (dict): interventional data to analyse in the form {INTERVENTION_VARIABLE : Data (same variables of observation_data)}.
        max_lag (int): maximum time lag.
        sel_method (SelectionMethod): selection method.
        val_condtest (CondIndTest): validation method.
        verbosity (CPLevel): verbosity level.
        f_alpha (float, optional): filter significance level. Defaults to 0.05.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        exclude_context (bool, optional): Bit for neglecting context variables. Defaults to False.
        plot_data (bool, optional): Bit for plotting your data. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    min_lag = 0

    self.obs_data = observation_data
    self.systems = observation_data.features
    self.contexts = []
    self.sys_context = {}
    for k in intervention_data.keys():
        self.contexts.append("C" + k)
        self.sys_context[k] = "C" + k
    self.vars = self.systems + self.contexts

    self.f_alpha = f_alpha
    self.sel_method = sel_method
    self.val_condtest = val_condtest
    self.exclude_context = exclude_context
    super().__init__(self.obs_data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

    # Create filter and validator data
    self.filter_data, self.validator_data = self._prepare_data(self.obs_data, intervention_data, plot_data)


    CP.info("\n")
    CP.info(DASH)
    CP.info("Observational data length: " + str(observation_data.T))
    CP.info("Interventional data length: " + str(sum([d.T for d in intervention_data.values()])))
    CP.info("Min lag time: " + str(min_lag))
    CP.info("Max lag time: " + str(max_lag))
    CP.info("Filter significance level: " + str(f_alpha))
    CP.info("PCMCI significance level: " + str(alpha))
    CP.info("Selection method: " + sel_method.name)

load(res_path)

Load previously estimated result.

Parameters:

Name Type Description Default
res_path str

pickle file path.

 required
Source code in causalflow/causal_discovery/CAnDOIT.py 
242
243
244
245
246
247
248
249
250
251
252
253
254
255
def load(self, res_path):
    """
    Load previously estimated result.

    Args:
        res_path (str): pickle file path.
    """
    with open(res_path, 'rb') as f:
        r = pickle.load(f)
        self.CM = r['causal_model']
        self.f_alpha = r['filter_alpha']
        self.alpha = r['alpha']
        self.dag_path = r['dag_path']
        self.ts_dag_path = r['ts_dag_path']

run(remove_unneeded=True, nofilter=True)

Run CAnDOIT.

Parameters:

Name Type Description Default
remove_unneeded bool

Bit to remove unneeded (isolated) variables. Defaults to True.

 True
nofilter bool

Bit to run CAnDOIT without filter. Defaults to False.

 True

Returns:

Name Type Description
DAG DAG

causal model.
Source code in causalflow/causal_discovery/CAnDOIT.py 
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
def run(self, remove_unneeded = True, nofilter = True) -> DAG:
    """
    Run CAnDOIT.

    Args:
        remove_unneeded (bool, optional): Bit to remove unneeded (isolated) variables. Defaults to True.
        nofilter (bool, optional): Bit to run CAnDOIT without filter. Defaults to False.

    Returns:
        DAG: causal model.
    """
    link_assumptions = None

    if not nofilter:
        #FIXME: to include also the filter. for now this is wrong
        ## 1. FILTER
        self.run_filter()

        # list of selected features based on filter dependencies
        self.CM.remove_unneeded_features()
        if not self.CM.features: return None, None

        self.obs_data.shrink(self.CM.features)
        f_dag = copy.deepcopy(self.CM)

        ## 2. VALIDATOR
        # Add dependencies corresponding to the context variables 
        # ONLY if the the related system variable is still present
        self.CM.add_context() 

        # shrink dataframe d by using the filter result
        self.validator_data.shrink(self.CM.features)

        # selected links to check by the validator
        link_assumptions = self.CM.get_link_assumptions()

    else:
        # fullg = DAG(self.validator_data.features, self.min_lag, self.max_lag, False)
        # fullg.sys_context = self.CM.sys_context
        link_assumptions = self.JCI_assumptions()

    # calculate dependencies on selected links
    self.CM = self.run_validator(link_assumptions)

    # list of selected features based on validator dependencies
    if remove_unneeded: self.CM.remove_unneeded_features()
    if self.exclude_context: self.CM.remove_context()

    self.save()

    return self.CM

run_filter()

Run filter method.

Source code in causalflow/causal_discovery/CAnDOIT.py 
159
160
161
162
163
164
def run_filter(self):
    """Run filter method."""
    CP.info("Selecting relevant features among: " + str(self.filter_data.features))

    self.sel_method.initialise(self.obs_data, self.f_alpha, self.min_lag, self.max_lag, self.CM)
    self.CM = self.sel_method.compute_dependencies()

run_validator(link_assumptions=None)

Run Validator (LPCMCI).

Parameters:

Name Type Description Default
link_assumptions dict

link assumption with context. Defaults to None.

 None

Returns:

Name Type Description
DAG DAG

causal model with context.
Source code in causalflow/causal_discovery/CAnDOIT.py 
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
def run_validator(self, link_assumptions = None) -> DAG:
    """
    Run Validator (LPCMCI).

    Args:
        link_assumptions (dict, optional): link assumption with context. Defaults to None.

    Returns:
        DAG: causal model with context.
    """
    self.validator = myLPCMCI(self.validator_data,
                            self.max_lag,
                            self.sys_context,
                            self.val_condtest,
                            CP.verbosity,
                            self.alpha)
    causal_model = self.validator.run(link_assumptions)
    causal_model.sys_context = self.CM.sys_context      

    return causal_model

save()

Save causal discovery result as pickle file if resfolder is set.

Source code in causalflow/causal_discovery/CAnDOIT.py 
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
def save(self):
    """Save causal discovery result as pickle file if resfolder is set."""
    if self.respath is not None:
        if self.CM:
            res = dict()
            res['causal_model'] = copy.deepcopy(self.CM)
            res['features'] = copy.deepcopy(self.CM.features)
            res['filter_alpha'] = self.f_alpha
            res['alpha'] = self.alpha
            res['dag_path'] = self.dag_path
            res['ts_dag_path'] = self.ts_dag_path
            with open(self.respath, 'wb') as resfile:
                pickle.dump(res, resfile)
        else:
            CP.warning("Causal model impossible to save")

This module provides the DYNOTEARS class.

Classes

DYNOTEARS: class containing the DYNOTEARS causal discovery algorithm.

DYNOTEARS

Bases: CausalDiscoveryMethod

DYNOTEARS causal discovery method.

Source code in causalflow/causal_discovery/baseline/DYNOTEARS.py 
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
class DYNOTEARS(CausalDiscoveryMethod):
    """DYNOTEARS causal discovery method."""

    def __init__(self, 
                 data, 
                 min_lag,
                 max_lag, 
                 verbosity, 
                 alpha = 0.05, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            verbosity (CPLevel): verbosity level.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

    def run(self) -> DAG:
        """
        Run DYNOTEARS algorithm.

        Returns:
            DAG: causal discovery result.
        """
        graph_dict = dict()
        for name in self.data.features:
            graph_dict[name] = []
        sm = from_pandas_dynamic(self.data.d, p=self.max_lag)

        tname_to_name_dict = dict()
        count_lag = 0
        idx_name = 0
        for tname in sm.nodes:
            tname_to_name_dict[tname] = self.data.features[idx_name]
            if count_lag == self.max_lag:
                idx_name = idx_name +1
                count_lag = -1
            count_lag = count_lag +1

        for ce in sm.edges:
            c = ce[0]
            e = ce[1]
            w = sm.adj[c][e]["weight"]
            tc = int(c.partition("lag")[2])
            te = int(e.partition("lag")[2])
            t = tc - te
            if (tname_to_name_dict[c], -t) not in graph_dict[tname_to_name_dict[e]]:
                graph_dict[tname_to_name_dict[e]].append((tname_to_name_dict[c], w, -t))

        self.CM = self._to_DAG(graph_dict)

        if self.resfolder is not None: self.logger.close()
        return self.CM


    def _to_DAG(self, graph):
        """
        Re-elaborate the result in a DAG.            

        Args:
            graph (dict): graph to convert into a DAG

        Returns:
            (DAG): result re-elaborated.
        """
        tmp_dag = DAG(self.data.features, self.min_lag, self.max_lag, self.neglect_only_autodep)
        tmp_dag.sys_context = dict()
        for t in graph.keys():
            for s in graph[t]:
                lag = abs(s[2])
                if lag >= self.min_lag and lag <= self.max_lag:
                    tmp_dag.add_source(t, s[0], abs(s[1]), 0, s[2])
        return tmp_dag

__init__(data, min_lag, max_lag, verbosity, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
verbosity CPLevel

verbosity level.

 required
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/DYNOTEARS.py 
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
def __init__(self, 
             data, 
             min_lag,
             max_lag, 
             verbosity, 
             alpha = 0.05, 
             resfolder = None,
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        verbosity (CPLevel): verbosity level.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

run()

Run DYNOTEARS algorithm.

Returns:

Name Type Description
DAG DAG

causal discovery result.
Source code in causalflow/causal_discovery/baseline/DYNOTEARS.py 
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
def run(self) -> DAG:
    """
    Run DYNOTEARS algorithm.

    Returns:
        DAG: causal discovery result.
    """
    graph_dict = dict()
    for name in self.data.features:
        graph_dict[name] = []
    sm = from_pandas_dynamic(self.data.d, p=self.max_lag)

    tname_to_name_dict = dict()
    count_lag = 0
    idx_name = 0
    for tname in sm.nodes:
        tname_to_name_dict[tname] = self.data.features[idx_name]
        if count_lag == self.max_lag:
            idx_name = idx_name +1
            count_lag = -1
        count_lag = count_lag +1

    for ce in sm.edges:
        c = ce[0]
        e = ce[1]
        w = sm.adj[c][e]["weight"]
        tc = int(c.partition("lag")[2])
        te = int(e.partition("lag")[2])
        t = tc - te
        if (tname_to_name_dict[c], -t) not in graph_dict[tname_to_name_dict[e]]:
            graph_dict[tname_to_name_dict[e]].append((tname_to_name_dict[c], w, -t))

    self.CM = self._to_DAG(graph_dict)

    if self.resfolder is not None: self.logger.close()
    return self.CM

This module provides the LPCMCI class.

Classes

LPCMCI: class containing the LPCMCI causal discovery algorithm.

LPCMCI

Bases: CausalDiscoveryMethod

LPCMCI causal discovery method.

Source code in causalflow/causal_discovery/baseline/LPCMCI.py 
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
class LPCMCI(CausalDiscoveryMethod):
    """LPCMCI causal discovery method."""

    def __init__(self, 
                 data: Data,
                 min_lag, max_lag, 
                 val_condtest: CondIndTest, 
                 verbosity: CPLevel,
                 alpha = 0.05, 
                 resfolder = None, 
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            val_condtest (CondIndTest): validation method.
            verbosity (CPLevel): verbosity level.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

        # build tigramite dataset
        vector = np.vectorize(float)
        d = vector(data.d)

        # init pcmci
        self.lpcmci = lpcmci(dataframe = pp.DataFrame(data = d, var_names = data.features),
                             cond_ind_test = val_condtest,
                             verbosity = verbosity.value)


    def run(self, link_assumptions = None) -> DAG:
        """
        Run causal discovery algorithm.

        Args:
            link_assumptions (dict, optional): prior knowledge on causal model links. Defaults to None.

        Returns:
            (DAG): estimated causal model.
        """
        CP.info('\n')
        CP.info(DASH)
        CP.info("Running Causal Discovery Algorithm")
        self.result = self.lpcmci.run_lpcmci(link_assumptions = link_assumptions,
                                             tau_max = self.max_lag,
                                             tau_min = self.min_lag,
                                             pc_alpha = self.alpha)

        self.CM = self._to_DAG()

        if self.resfolder is not None: self.logger.close()
        return self.CM


    def _to_DAG(self):
        """
        Re-elaborate the PCMCI result in a new dictionary.

        Returns:
            (DAG): lpcmci result re-elaborated.
        """
        vars = self.data.features
        tmp_dag = DAG(vars, self.min_lag, self.max_lag)
        tmp_dag.sys_context = dict()
        N, lags = self.result['graph'][0].shape
        for s in range(len(self.result['graph'])):
            for t in range(N):
                for lag in range(lags):
                    if self.result['graph'][s][t,lag] != '':
                        arrowtype = self.result['graph'][s][t,lag]

                        if arrowtype == LinkType.Bidirected.value:
                            if ((vars[s], abs(lag)) in tmp_dag.g[vars[t]].sources and 
                                tmp_dag.g[t].sources[(vars[s], abs(lag))][TYPE] == LinkType.Bidirected.value):
                                continue
                            else:
                                tmp_dag.add_source(vars[t], 
                                                vars[s],
                                                self.result['val_matrix'][s][t,lag],
                                                self.result['p_matrix'][s][t,lag],
                                                lag,
                                                arrowtype)


                        elif arrowtype == LinkType.Uncertain.value:
                            if ((vars[t], abs(lag)) in tmp_dag.g[vars[s]].sources and 
                                tmp_dag.g[vars[s]].sources[(vars[t], abs(lag))][TYPE] == LinkType.Uncertain.value):
                                continue
                            else:
                                tmp_dag.add_source(vars[t], 
                                                vars[s],
                                                self.result['val_matrix'][s][t,lag],
                                                self.result['p_matrix'][s][t,lag],
                                                lag,
                                                arrowtype)


                        elif (arrowtype == LinkType.Directed.value or
                              arrowtype == LinkType.HalfUncertain.value):
                            tmp_dag.add_source(vars[t], 
                                            vars[s],
                                            self.result['val_matrix'][s][t,lag],
                                            self.result['p_matrix'][s][t,lag],
                                            lag,
                                            arrowtype)
        return tmp_dag

__init__(data, min_lag, max_lag, val_condtest, verbosity, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
val_condtest CondIndTest

validation method.

 required
verbosity CPLevel

verbosity level.

 required
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/LPCMCI.py 
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
def __init__(self, 
             data: Data,
             min_lag, max_lag, 
             val_condtest: CondIndTest, 
             verbosity: CPLevel,
             alpha = 0.05, 
             resfolder = None, 
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        val_condtest (CondIndTest): validation method.
        verbosity (CPLevel): verbosity level.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

    # build tigramite dataset
    vector = np.vectorize(float)
    d = vector(data.d)

    # init pcmci
    self.lpcmci = lpcmci(dataframe = pp.DataFrame(data = d, var_names = data.features),
                         cond_ind_test = val_condtest,
                         verbosity = verbosity.value)

run(link_assumptions=None)

Run causal discovery algorithm.

Parameters:

Name Type Description Default
link_assumptions dict

prior knowledge on causal model links. Defaults to None.

 None

Returns:

Type Description
DAG

estimated causal model.
Source code in causalflow/causal_discovery/baseline/LPCMCI.py 
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
def run(self, link_assumptions = None) -> DAG:
    """
    Run causal discovery algorithm.

    Args:
        link_assumptions (dict, optional): prior knowledge on causal model links. Defaults to None.

    Returns:
        (DAG): estimated causal model.
    """
    CP.info('\n')
    CP.info(DASH)
    CP.info("Running Causal Discovery Algorithm")
    self.result = self.lpcmci.run_lpcmci(link_assumptions = link_assumptions,
                                         tau_max = self.max_lag,
                                         tau_min = self.min_lag,
                                         pc_alpha = self.alpha)

    self.CM = self._to_DAG()

    if self.resfolder is not None: self.logger.close()
    return self.CM

This module provides the PCMCI class.

Classes

PCMCI: class containing the PCMCI causal discovery algorithm.

PCMCI

Bases: CausalDiscoveryMethod

PCMCI causal discovery method.

Source code in causalflow/causal_discovery/baseline/PCMCI.py 
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
class PCMCI(CausalDiscoveryMethod):
    """PCMCI causal discovery method."""

    def __init__(self, 
                 data: Data, 
                 min_lag, max_lag, 
                 val_condtest: CondIndTest, 
                 verbosity: CPLevel,
                 pc_alpha = 0.05, 
                 alpha = 0.05, 
                 resfolder = None, 
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            val_condtest (CondIndTest): validation method.
            verbosity (CPLevel): verbosity level.
            pc_alpha (float, optional): PC significance level. Defaults to 0.05.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)
        self.pc_alpha = pc_alpha

        # build tigramite dataset
        vector = np.vectorize(float)
        d = vector(data.d)

        # init pcmci
        self.pcmci = pcmci(dataframe = pp.DataFrame(data = d, var_names = data.features),
                           cond_ind_test = val_condtest,
                           verbosity = verbosity.value)


    def run(self, link_assumptions = None) -> DAG:
        """
        Run causal discovery algorithm.

        Args:
            link_assumptions (dict, optional): prior knowledge on causal model links. Defaults to None.

        Returns:
            (DAG): estimated causal model.
        """
        CP.info('\n')
        CP.info(DASH)
        CP.info("Running Causal Discovery Algorithm")

        self.result = self.pcmci.run_pcmci(link_assumptions=link_assumptions,
                                           tau_max = self.max_lag,
                                           tau_min = self.min_lag,
                                           alpha_level = self.alpha,
                                           pc_alpha = self.pc_alpha)

        self.CM = self._to_DAG()

        if self.resfolder is not None: self.logger.close()        
        return self.CM


    def _to_DAG(self):
        """
        Re-elaborates the PCMCI result in a new dictionary.

        Returns:
            (DAG): pcmci result re-elaborated.
        """
        vars = self.data.features
        tmp_dag = DAG(vars, self.min_lag, self.max_lag)
        tmp_dag.sys_context = dict()
        N, lags = self.result['graph'][0].shape
        for s in range(len(self.result['graph'])):
            for t in range(N):
                for lag in range(lags):
                    if self.result['graph'][s][t,lag] != '':
                        arrowtype = self.result['graph'][s][t,lag]

                        if arrowtype == LinkType.Bidirected.value:
                            if ((vars[s], abs(lag)) in tmp_dag.g[vars[t]].sources and 
                                tmp_dag.g[t].sources[(vars[s], abs(lag))][TYPE] == LinkType.Bidirected.value):
                                continue
                            else:
                                tmp_dag.add_source(vars[t], 
                                                vars[s],
                                                self.result['val_matrix'][s][t,lag],
                                                self.result['p_matrix'][s][t,lag],
                                                lag,
                                                arrowtype)


                        elif arrowtype == LinkType.Uncertain.value:
                            if ((vars[t], abs(lag)) in tmp_dag.g[vars[s]].sources and 
                                tmp_dag.g[vars[s]].sources[(vars[t], abs(lag))][TYPE] == LinkType.Uncertain.value):
                                continue
                            else:
                                tmp_dag.add_source(vars[t], 
                                                vars[s],
                                                self.result['val_matrix'][s][t,lag],
                                                self.result['p_matrix'][s][t,lag],
                                                lag,
                                                arrowtype)


                        elif (arrowtype == LinkType.Directed.value or
                              arrowtype == LinkType.HalfUncertain.value):
                            tmp_dag.add_source(vars[t], 
                                            vars[s],
                                            self.result['val_matrix'][s][t,lag],
                                            self.result['p_matrix'][s][t,lag],
                                            lag,
                                            arrowtype)
        return tmp_dag

__init__(data, min_lag, max_lag, val_condtest, verbosity, pc_alpha=0.05, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
val_condtest CondIndTest

validation method.

 required
verbosity CPLevel

verbosity level.

 required
pc_alpha float

PC significance level. Defaults to 0.05.

 0.05
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/PCMCI.py 
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
def __init__(self, 
             data: Data, 
             min_lag, max_lag, 
             val_condtest: CondIndTest, 
             verbosity: CPLevel,
             pc_alpha = 0.05, 
             alpha = 0.05, 
             resfolder = None, 
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        val_condtest (CondIndTest): validation method.
        verbosity (CPLevel): verbosity level.
        pc_alpha (float, optional): PC significance level. Defaults to 0.05.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)
    self.pc_alpha = pc_alpha

    # build tigramite dataset
    vector = np.vectorize(float)
    d = vector(data.d)

    # init pcmci
    self.pcmci = pcmci(dataframe = pp.DataFrame(data = d, var_names = data.features),
                       cond_ind_test = val_condtest,
                       verbosity = verbosity.value)

run(link_assumptions=None)

Run causal discovery algorithm.

Parameters:

Name Type Description Default
link_assumptions dict

prior knowledge on causal model links. Defaults to None.

 None

Returns:

Type Description
DAG

estimated causal model.
Source code in causalflow/causal_discovery/baseline/PCMCI.py 
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
def run(self, link_assumptions = None) -> DAG:
    """
    Run causal discovery algorithm.

    Args:
        link_assumptions (dict, optional): prior knowledge on causal model links. Defaults to None.

    Returns:
        (DAG): estimated causal model.
    """
    CP.info('\n')
    CP.info(DASH)
    CP.info("Running Causal Discovery Algorithm")

    self.result = self.pcmci.run_pcmci(link_assumptions=link_assumptions,
                                       tau_max = self.max_lag,
                                       tau_min = self.min_lag,
                                       alpha_level = self.alpha,
                                       pc_alpha = self.pc_alpha)

    self.CM = self._to_DAG()

    if self.resfolder is not None: self.logger.close()        
    return self.CM

This module provides the PCMCI+ class.

Classes

PCMCIplus: class containing the PCMCI+ causal discovery algorithm.

PCMCIplus

Bases: CausalDiscoveryMethod

PCMCI+ causal discovery method.

Source code in causalflow/causal_discovery/baseline/PCMCIplus.py 
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
class PCMCIplus(CausalDiscoveryMethod):
    """PCMCI+ causal discovery method."""

    def __init__(self, 
                 data: Data, 
                 min_lag, max_lag, 
                 val_condtest: CondIndTest, 
                 verbosity: CPLevel,
                 alpha = 0.05, 
                 resfolder = None, 
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            val_condtest (CondIndTest): validation method.
            verbosity (CPLevel): verbosity level.
            alpha (float, optional): significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

        # build tigramite dataset
        vector = np.vectorize(float)
        d = vector(data.d)

        # init pcmci
        self.pcmci = pcmci(dataframe = pp.DataFrame(data = d, var_names = data.features),
                           cond_ind_test = val_condtest,
                           verbosity = verbosity.value)


    def run(self, link_assumptions=None) -> DAG:
        """
        Run causal discovery algorithm.

        Args:
            link_assumptions (dict, optional): prior knowledge on causal model links. Defaults to None.

        Returns:
            (DAG): estimated causal model.
        """
        CP.info('\n')
        CP.info(DASH)
        CP.info("Running Causal Discovery Algorithm")

        self.result = self.pcmci.run_pcmciplus(link_assumptions=link_assumptions,
                                               tau_max = self.max_lag,
                                               tau_min = 0,
                                               pc_alpha = self.alpha)

        self.CM = self._to_DAG()

        if self.resfolder is not None: self.logger.close()
        return self.CM


    def _to_DAG(self):
        """
        Re-elaborates the PCMCI result in a new dictionary.

        Returns:
            (DAG): pcmci result re-elaborated.
        """
        vars = self.data.features
        tmp_dag = DAG(vars, self.min_lag, self.max_lag)
        tmp_dag.sys_context = dict()
        N, lags = self.result['graph'][0].shape
        for s in range(len(self.result['graph'])):
            for t in range(N):
                for lag in range(lags):
                    if self.result['graph'][s][t,lag] != '':
                        arrowtype = self.result['graph'][s][t,lag]

                        if arrowtype == LinkType.Bidirected.value:
                            if ((vars[s], abs(lag)) in tmp_dag.g[vars[t]].sources and 
                                tmp_dag.g[t].sources[(vars[s], abs(lag))][TYPE] == LinkType.Bidirected.value):
                                continue
                            else:
                                tmp_dag.add_source(vars[t], 
                                                vars[s],
                                                self.result['val_matrix'][s][t,lag],
                                                self.result['p_matrix'][s][t,lag],
                                                lag,
                                                arrowtype)


                        elif arrowtype == LinkType.Uncertain.value:
                            if ((vars[t], abs(lag)) in tmp_dag.g[vars[s]].sources and 
                                tmp_dag.g[vars[s]].sources[(vars[t], abs(lag))][TYPE] == LinkType.Uncertain.value):
                                continue
                            else:
                                tmp_dag.add_source(vars[t], 
                                                vars[s],
                                                self.result['val_matrix'][s][t,lag],
                                                self.result['p_matrix'][s][t,lag],
                                                lag,
                                                arrowtype)


                        elif (arrowtype == LinkType.Directed.value or
                              arrowtype == LinkType.HalfUncertain.value):
                            tmp_dag.add_source(vars[t], 
                                            vars[s],
                                            self.result['val_matrix'][s][t,lag],
                                            self.result['p_matrix'][s][t,lag],
                                            lag,
                                            arrowtype)
        return tmp_dag

__init__(data, min_lag, max_lag, val_condtest, verbosity, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
val_condtest CondIndTest

validation method.

 required
verbosity CPLevel

verbosity level.

 required
alpha float

significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/PCMCIplus.py 
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
def __init__(self, 
             data: Data, 
             min_lag, max_lag, 
             val_condtest: CondIndTest, 
             verbosity: CPLevel,
             alpha = 0.05, 
             resfolder = None, 
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        val_condtest (CondIndTest): validation method.
        verbosity (CPLevel): verbosity level.
        alpha (float, optional): significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

    # build tigramite dataset
    vector = np.vectorize(float)
    d = vector(data.d)

    # init pcmci
    self.pcmci = pcmci(dataframe = pp.DataFrame(data = d, var_names = data.features),
                       cond_ind_test = val_condtest,
                       verbosity = verbosity.value)

run(link_assumptions=None)

Run causal discovery algorithm.

Parameters:

Name Type Description Default
link_assumptions dict

prior knowledge on causal model links. Defaults to None.

 None

Returns:

Type Description
DAG

estimated causal model.
Source code in causalflow/causal_discovery/baseline/PCMCIplus.py 
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
def run(self, link_assumptions=None) -> DAG:
    """
    Run causal discovery algorithm.

    Args:
        link_assumptions (dict, optional): prior knowledge on causal model links. Defaults to None.

    Returns:
        (DAG): estimated causal model.
    """
    CP.info('\n')
    CP.info(DASH)
    CP.info("Running Causal Discovery Algorithm")

    self.result = self.pcmci.run_pcmciplus(link_assumptions=link_assumptions,
                                           tau_max = self.max_lag,
                                           tau_min = 0,
                                           pc_alpha = self.alpha)

    self.CM = self._to_DAG()

    if self.resfolder is not None: self.logger.close()
    return self.CM

This module provides the TCDF class.

Classes

TCDF: class containing the TCDF causal discovery algorithm.

TCDF

Bases: CausalDiscoveryMethod

TCDF causal discovery method.

Source code in causalflow/causal_discovery/baseline/TCDF.py 
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
class TCDF(CausalDiscoveryMethod):
    """TCDF causal discovery method."""

    def __init__(self, 
                 data, 
                 min_lag,
                 max_lag, 
                 verbosity, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            verbosity (CPLevel): verbosity level.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, resfolder=resfolder, neglect_only_autodep=neglect_only_autodep, clean_cls=clean_cls)


    def run(self, 
            epochs=1000,  
            kernel_size=4, 
            dilation_coefficient=4, 
            hidden_layers=0, 
            learning_rate=0.01,
            cuda=False) -> DAG:
        """
        Run causal discovery algorithm.

        Returns:
            (DAG): estimated causal model.
        """
        # Remove all arguments from directory
        dir_path = os.path.dirname(os.path.realpath(__file__))
        Path(dir_path+"/args").mkdir(exist_ok=True)
        Path(dir_path+"/results").mkdir(exist_ok=True)
        script = dir_path + "/pkgs/TCDF-master/runTCDF" + ".py"
        r_arg_list = []
        r_arg_list.append("--epochs")
        r_arg_list.append(str(epochs))
        r_arg_list.append("--kernel_size")
        r_arg_list.append(str(kernel_size))
        r_arg_list.append("--dilation_coefficient")
        r_arg_list.append(str(dilation_coefficient))
        r_arg_list.append("--hidden_layers")
        r_arg_list.append(str(hidden_layers))
        r_arg_list.append("--learning_rate")
        r_arg_list.append(str(learning_rate))
        r_arg_list.append("--significance")
        r_arg_list.append(str(0.8))
        self.data.d.to_csv(dir_path + "/args/data.csv", index=False)
        r_arg_list.append("--data")
        r_arg_list.append(dir_path + "/args/data.csv")            
        if cuda: r_arg_list.append("--cuda")
        r_arg_list.append("--path")
        r_arg_list.append(dir_path)

        cmd = ["python", script] + r_arg_list
        p = Popen(cmd, cwd="./", stdin=PIPE, stdout=PIPE, stderr=PIPE)

        # Return R output or error
        output, error = p.communicate()
        CP.info(output.decode('utf-8'))
        if p.returncode == 0:
            g_dict = json.load(open(dir_path + "/results/tcdf_result.txt"))
            for key in g_dict.keys():
                key_list = []
                for elem in g_dict[key]:
                    key_list.append(tuple(elem))
                g_dict[key] = key_list
            utils.clean(dir_path)
            self.CM = self._to_DAG(g_dict)

            if self.resfolder is not None: self.logger.close()
            return self.CM
        else:
            utils.clean(dir_path)
            CP.warning('Python Error:\n {0}'.format(error))
            exit(0)


    def _to_DAG(self, graph):
        """
        Re-elaborate the result in a DAG.

        Args:
            graph (dict): graph to convert into a DAG

        Returns:
            (DAG): result re-elaborated.
        """
        tmp_dag = DAG(self.data.features, self.min_lag, self.max_lag, self.neglect_only_autodep)
        tmp_dag.sys_context = dict()
        for t in graph.keys():
            for s in graph[t]:
                lag = abs(s[1])
                if lag >= self.min_lag and lag <= self.max_lag:
                    tmp_dag.add_source(t, s[0], utils.DSCORE, 0, s[1])
        return tmp_dag

__init__(data, min_lag, max_lag, verbosity, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
verbosity CPLevel

verbosity level.

 required
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/TCDF.py 
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
def __init__(self, 
             data, 
             min_lag,
             max_lag, 
             verbosity, 
             resfolder = None,
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        verbosity (CPLevel): verbosity level.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, resfolder=resfolder, neglect_only_autodep=neglect_only_autodep, clean_cls=clean_cls)

run(epochs=1000, kernel_size=4, dilation_coefficient=4, hidden_layers=0, learning_rate=0.01, cuda=False)

Run causal discovery algorithm.

Returns:

Type Description
DAG

estimated causal model.
Source code in causalflow/causal_discovery/baseline/TCDF.py 
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
def run(self, 
        epochs=1000,  
        kernel_size=4, 
        dilation_coefficient=4, 
        hidden_layers=0, 
        learning_rate=0.01,
        cuda=False) -> DAG:
    """
    Run causal discovery algorithm.

    Returns:
        (DAG): estimated causal model.
    """
    # Remove all arguments from directory
    dir_path = os.path.dirname(os.path.realpath(__file__))
    Path(dir_path+"/args").mkdir(exist_ok=True)
    Path(dir_path+"/results").mkdir(exist_ok=True)
    script = dir_path + "/pkgs/TCDF-master/runTCDF" + ".py"
    r_arg_list = []
    r_arg_list.append("--epochs")
    r_arg_list.append(str(epochs))
    r_arg_list.append("--kernel_size")
    r_arg_list.append(str(kernel_size))
    r_arg_list.append("--dilation_coefficient")
    r_arg_list.append(str(dilation_coefficient))
    r_arg_list.append("--hidden_layers")
    r_arg_list.append(str(hidden_layers))
    r_arg_list.append("--learning_rate")
    r_arg_list.append(str(learning_rate))
    r_arg_list.append("--significance")
    r_arg_list.append(str(0.8))
    self.data.d.to_csv(dir_path + "/args/data.csv", index=False)
    r_arg_list.append("--data")
    r_arg_list.append(dir_path + "/args/data.csv")            
    if cuda: r_arg_list.append("--cuda")
    r_arg_list.append("--path")
    r_arg_list.append(dir_path)

    cmd = ["python", script] + r_arg_list
    p = Popen(cmd, cwd="./", stdin=PIPE, stdout=PIPE, stderr=PIPE)

    # Return R output or error
    output, error = p.communicate()
    CP.info(output.decode('utf-8'))
    if p.returncode == 0:
        g_dict = json.load(open(dir_path + "/results/tcdf_result.txt"))
        for key in g_dict.keys():
            key_list = []
            for elem in g_dict[key]:
                key_list.append(tuple(elem))
            g_dict[key] = key_list
        utils.clean(dir_path)
        self.CM = self._to_DAG(g_dict)

        if self.resfolder is not None: self.logger.close()
        return self.CM
    else:
        utils.clean(dir_path)
        CP.warning('Python Error:\n {0}'.format(error))
        exit(0)

This module provides the tsFCI class.

Classes

tsFCI: class containing the tsFCI causal discovery algorithm.

tsFCI

Bases: CausalDiscoveryMethod

tsFCI causal discovery method.

Source code in causalflow/causal_discovery/baseline/tsFCI.py 
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
class tsFCI(CausalDiscoveryMethod):
    """tsFCI causal discovery method."""

    def __init__(self, 
                 data, 
                 min_lag,
                 max_lag, 
                 verbosity, 
                 alpha = 0.05, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            verbosity (CPLevel): verbosity level.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)


    def run(self) -> DAG:
        """
        Run causal discovery algorithm.

        Returns:
            (DAG): estimated causal model.
        """
        # Remove all arguments from directory
        dir_path = os.path.dirname(os.path.realpath(__file__))
        Path(dir_path + "/args").mkdir(exist_ok=True)
        Path(dir_path + "/results").mkdir(exist_ok=True)

        script = dir_path + "/pkgs/tsfci.R"
        r_arg_list = []

        # COMMAND WITH ARGUMENTS
        self.data.d.to_csv(dir_path + "/args/data.csv", index=False)
        r_arg_list.append(dir_path + "/args/data.csv")
        r_arg_list.append(str(self.alpha))
        r_arg_list.append(str(self.max_lag))

        r_arg_list.append(dir_path)
        cmd = ["Rscript", script] + r_arg_list

        p = Popen(cmd, cwd="./", stdin=PIPE, stdout=PIPE, stderr=PIPE)

        # Return R output or error
        output, error = p.communicate()
        print(output.decode('utf-8'))
        if p.returncode == 0:
            g_df = pd.read_csv(dir_path + "/results/result.csv", header=0, index_col=0)
            g_dict = self.ts_fci_dataframe_to_dict(g_df, self.data.features, self.max_lag)
            self.CM = self._to_DAG(g_dict)
            utils.clean(dir_path)

            if self.resfolder is not None: self.logger.close()
            return self.CM

        else:
            utils.clean(dir_path)
            print('R Error:\n {0}'.format(error.decode('utf-8')))
            exit(0)


    def _to_DAG(self, graph):
        """
        Re-elaborate the result in a DAG.

        Args:
            graph (dict): graph to convert into a DAG

        Returns:
            (DAG): result re-elaborated.
        """
        tmp_dag = DAG(self.data.features, self.min_lag, self.max_lag, self.neglect_only_autodep)
        tmp_dag.sys_context = dict()
        for t in graph.keys():
            for s in graph[t]:
                lag = abs(s[1])
                if lag >= self.min_lag and lag <= self.max_lag:
                    tmp_dag.add_source(t, s[0], utils.DSCORE, 0, s[1])
        return tmp_dag


    def ts_fci_dataframe_to_dict(self, df, names, nlags) -> dict:
        """
        Convert tsFCI result into a dict for _to_DAG.

        Args:
            df (DataFrame): graph.
            names (list[str]): variables' name.
            nlags (int): max time lag.

        Returns:
            dict: dict graph.
        """
        # todo: check if its correct
        for i in range(df.shape[1]):
            for j in range(i+1, df.shape[1]):
                if df[df.columns[i]].loc[df.columns[j]] == 2:
                    if df[df.columns[j]].loc[df.columns[i]] == 2:
                        print(df.columns[i] + " <-> " + df.columns[j])

        g_dict = dict()
        for name_y in names:
            g_dict[name_y] = []
        for ty in range(nlags):
            for name_y in names:
                t_name_y = df.columns[ty*len(names)+names.index(name_y)]
                for tx in range(nlags):
                    for name_x in names:
                        t_name_x = df.columns[tx * len(names) + names.index(name_x)]
                        if df[t_name_y].loc[t_name_x] == 2:
                            if (name_x, tx-ty) not in g_dict[name_y]:
                                g_dict[name_y].append((name_x, tx - ty))
        print(g_dict)
        return g_dict

__init__(data, min_lag, max_lag, verbosity, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
verbosity CPLevel

verbosity level.

 required
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/tsFCI.py 
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
def __init__(self, 
             data, 
             min_lag,
             max_lag, 
             verbosity, 
             alpha = 0.05, 
             resfolder = None,
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        verbosity (CPLevel): verbosity level.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

run()

Run causal discovery algorithm.

Returns:

Type Description
DAG

estimated causal model.
Source code in causalflow/causal_discovery/baseline/tsFCI.py 
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
def run(self) -> DAG:
    """
    Run causal discovery algorithm.

    Returns:
        (DAG): estimated causal model.
    """
    # Remove all arguments from directory
    dir_path = os.path.dirname(os.path.realpath(__file__))
    Path(dir_path + "/args").mkdir(exist_ok=True)
    Path(dir_path + "/results").mkdir(exist_ok=True)

    script = dir_path + "/pkgs/tsfci.R"
    r_arg_list = []

    # COMMAND WITH ARGUMENTS
    self.data.d.to_csv(dir_path + "/args/data.csv", index=False)
    r_arg_list.append(dir_path + "/args/data.csv")
    r_arg_list.append(str(self.alpha))
    r_arg_list.append(str(self.max_lag))

    r_arg_list.append(dir_path)
    cmd = ["Rscript", script] + r_arg_list

    p = Popen(cmd, cwd="./", stdin=PIPE, stdout=PIPE, stderr=PIPE)

    # Return R output or error
    output, error = p.communicate()
    print(output.decode('utf-8'))
    if p.returncode == 0:
        g_df = pd.read_csv(dir_path + "/results/result.csv", header=0, index_col=0)
        g_dict = self.ts_fci_dataframe_to_dict(g_df, self.data.features, self.max_lag)
        self.CM = self._to_DAG(g_dict)
        utils.clean(dir_path)

        if self.resfolder is not None: self.logger.close()
        return self.CM

    else:
        utils.clean(dir_path)
        print('R Error:\n {0}'.format(error.decode('utf-8')))
        exit(0)

ts_fci_dataframe_to_dict(df, names, nlags)

Convert tsFCI result into a dict for _to_DAG.

Parameters:

Name Type Description Default
df DataFrame

graph.

 required
names list[str]

variables' name.

 required
nlags int

max time lag.

 required

Returns:

Name Type Description
dict dict

dict graph.
Source code in causalflow/causal_discovery/baseline/tsFCI.py 
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
def ts_fci_dataframe_to_dict(self, df, names, nlags) -> dict:
    """
    Convert tsFCI result into a dict for _to_DAG.

    Args:
        df (DataFrame): graph.
        names (list[str]): variables' name.
        nlags (int): max time lag.

    Returns:
        dict: dict graph.
    """
    # todo: check if its correct
    for i in range(df.shape[1]):
        for j in range(i+1, df.shape[1]):
            if df[df.columns[i]].loc[df.columns[j]] == 2:
                if df[df.columns[j]].loc[df.columns[i]] == 2:
                    print(df.columns[i] + " <-> " + df.columns[j])

    g_dict = dict()
    for name_y in names:
        g_dict[name_y] = []
    for ty in range(nlags):
        for name_y in names:
            t_name_y = df.columns[ty*len(names)+names.index(name_y)]
            for tx in range(nlags):
                for name_x in names:
                    t_name_x = df.columns[tx * len(names) + names.index(name_x)]
                    if df[t_name_y].loc[t_name_x] == 2:
                        if (name_x, tx-ty) not in g_dict[name_y]:
                            g_dict[name_y].append((name_x, tx - ty))
    print(g_dict)
    return g_dict

This module provides the VarLiNGAM class.

Classes

VarLiNGAM: class containing the VarLiNGAM causal discovery algorithm.

VarLiNGAM

Bases: CausalDiscoveryMethod

VarLiNGAM causal discovery method.

Source code in causalflow/causal_discovery/baseline/VarLiNGAM.py 
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
class VarLiNGAM(CausalDiscoveryMethod):
    """VarLiNGAM causal discovery method."""

    def __init__(self, 
                 data, 
                 min_lag,
                 max_lag, 
                 verbosity, 
                 alpha = 0.05, 
                 resfolder = None,
                 neglect_only_autodep = False,
                 clean_cls = True):
        """
        Class constructor.

        Args:
            data (Data): data to analyse.
            min_lag (int): minimum time lag.
            max_lag (int): maximum time lag.
            verbosity (CPLevel): verbosity level.
            alpha (float, optional): PCMCI significance level. Defaults to 0.05.
            resfolder (string, optional): result folder to create. Defaults to None.
            neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
            clean_cls (bool): Clean console bit. Default to True.
        """
        super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)


    def run(self) -> DAG:
        """
        Run causal discovery algorithm.

        Returns:
            (DAG): estimated causal model.
        """
        split_by_causal_effect_sign = True

        model = VARLiNGAM(lags = self.max_lag, criterion='bic', prune=True)
        model.fit(self.data.d)

        m = model._adjacency_matrices
        am = np.concatenate([*m], axis=1)

        dag = np.abs(am) > self.alpha

        if split_by_causal_effect_sign:
            direction = np.array(np.where(dag))
            signs = np.zeros_like(dag).astype('int64')
            for i, j in direction.T:
                signs[i][j] = np.sign(am[i][j]).astype('int64')
            dag = signs

        dag = np.abs(dag)
        names = self.data.features
        res_dict = dict()
        for e in range(dag.shape[0]):
            res_dict[names[e]] = []
        for c in range(dag.shape[0]):
            for te in range(dag.shape[1]):
                if dag[c][te] == 1:
                    e = te%dag.shape[0]
                    t = te//dag.shape[0]
                    res_dict[names[e]].append((names[c], -t))
        self.CM = self._to_DAG(res_dict)

        if self.resfolder is not None: self.logger.close()
        return self.CM

    def _to_DAG(self, graph):
        """
        Re-elaborates the result in a DAG.

        Args:
            graph (dict): graph to convert into a DAG

        Returns:
            (DAG): result re-elaborated.
        """
        tmp_dag = DAG(self.data.features, self.min_lag, self.max_lag, self.neglect_only_autodep)
        tmp_dag.sys_context = dict()
        for t in graph.keys():
            for s in graph[t]:
                lag = abs(s[1])
                if lag >= self.min_lag and lag <= self.max_lag:
                    tmp_dag.add_source(t, s[0], utils.DSCORE, 0, s[1])
        return tmp_dag

__init__(data, min_lag, max_lag, verbosity, alpha=0.05, resfolder=None, neglect_only_autodep=False, clean_cls=True)

Class constructor.

Parameters:

Name Type Description Default
data Data

data to analyse.

 required
min_lag int

minimum time lag.

 required
max_lag int

maximum time lag.

 required
verbosity CPLevel

verbosity level.

 required
alpha float

PCMCI significance level. Defaults to 0.05.

 0.05
resfolder string

result folder to create. Defaults to None.

 None
neglect_only_autodep bool

Bit for neglecting variables with only autodependency. Defaults to False.

 False
clean_cls bool

Clean console bit. Default to True.

 True
Source code in causalflow/causal_discovery/baseline/VarLiNGAM.py 
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
def __init__(self, 
             data, 
             min_lag,
             max_lag, 
             verbosity, 
             alpha = 0.05, 
             resfolder = None,
             neglect_only_autodep = False,
             clean_cls = True):
    """
    Class constructor.

    Args:
        data (Data): data to analyse.
        min_lag (int): minimum time lag.
        max_lag (int): maximum time lag.
        verbosity (CPLevel): verbosity level.
        alpha (float, optional): PCMCI significance level. Defaults to 0.05.
        resfolder (string, optional): result folder to create. Defaults to None.
        neglect_only_autodep (bool, optional): Bit for neglecting variables with only autodependency. Defaults to False.
        clean_cls (bool): Clean console bit. Default to True.
    """
    super().__init__(data, min_lag, max_lag, verbosity, alpha, resfolder, neglect_only_autodep, clean_cls)

run()

Run causal discovery algorithm.

Returns:

Type Description
DAG

estimated causal model.
Source code in causalflow/causal_discovery/baseline/VarLiNGAM.py 
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
def run(self) -> DAG:
    """
    Run causal discovery algorithm.

    Returns:
        (DAG): estimated causal model.
    """
    split_by_causal_effect_sign = True

    model = VARLiNGAM(lags = self.max_lag, criterion='bic', prune=True)
    model.fit(self.data.d)

    m = model._adjacency_matrices
    am = np.concatenate([*m], axis=1)

    dag = np.abs(am) > self.alpha

    if split_by_causal_effect_sign:
        direction = np.array(np.where(dag))
        signs = np.zeros_like(dag).astype('int64')
        for i, j in direction.T:
            signs[i][j] = np.sign(am[i][j]).astype('int64')
        dag = signs

    dag = np.abs(dag)
    names = self.data.features
    res_dict = dict()
    for e in range(dag.shape[0]):
        res_dict[names[e]] = []
    for c in range(dag.shape[0]):
        for te in range(dag.shape[1]):
            if dag[c][te] == 1:
                e = te%dag.shape[0]
                t = te//dag.shape[0]
                res_dict[names[e]].append((names[c], -t))
    self.CM = self._to_DAG(res_dict)

    if self.resfolder is not None: self.logger.close()
    return self.CM