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Merge pull request #435 from jakobrunge/developer
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fixed issue with Graphs class graph checks
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@jakobrunge
jakobrunge authored Nov 28, 2024
2 parents 04d735c + 1357b65 commit 433c315
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Showing 2 changed files with 21 additions and 217 deletions.
2 changes: 1 addition & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def run(self):
# Run the setup
setup(
name="tigramite",
version="5.2.6.6",
version="5.2.6.7",
packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
license="GNU General Public License v3.0",
description="Tigramite causal inference for time series",
Expand Down
236 changes: 20 additions & 216 deletions tigramite/graphs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ class Graphs():
def __init__(self,
graph,
graph_type,
tau_max,
tau_max=0,
hidden_variables=None,
verbosity=0):

Expand Down Expand Up @@ -96,6 +96,12 @@ def _construct_graph(self, graph, graph_type, hidden_variables):
if graph_type in ['dag', 'admg']:
if graph.ndim != 2:
raise ValueError("graph_type in ['dag', 'admg'] assumes graph.shape=(N, N).")

allowed_edges = ["-->", "<--", "<->"]
if np.any(np.isin(graph, allowed_edges) == False):
raise ValueError("Graph contains invalid graph edge. " +
"For graph_type = %s only %s are allowed." %(graph_type, str(allowed_edges)))

# Convert to shape [N, N, 1, 1] with dummy dimension
# to process as tsg_dag or tsg_admg with potential hidden variables
self.graph = np.expand_dims(graph, axis=(2, 3))
Expand All @@ -114,6 +120,11 @@ def _construct_graph(self, graph, graph_type, hidden_variables):
if graph.ndim != 4:
raise ValueError("tsg-graph_type assumes graph.shape=(N, N, tau_max+1, tau_max+1).")

allowed_edges = ["-->", "<--", "<->"]
if np.any(np.isin(graph, allowed_edges) == False):
raise ValueError("Graph contains invalid graph edge. " +
"For graph_type = %s only %s are allowed." %(graph_type, str(allowed_edges)))

# Then tau_max is implicitely derived from
# the dimensions
self.graph = graph
Expand All @@ -130,6 +141,11 @@ def _construct_graph(self, graph, graph_type, hidden_variables):
if graph.ndim != 3:
raise ValueError("stationary graph_type assumes graph.shape=(N, N, tau_max+1).")

allowed_edges = ["-->", "<--", "<->"]
if np.any(np.isin(graph, allowed_edges) == False):
raise ValueError("Graph contains invalid graph edge. " +
"For graph_type = %s only %s are allowed." %(graph_type, str(allowed_edges)))

# # TODO: remove if theory for stationary ADMGs is clear
# if graph_type == 'stationary_dag' and len(hidden_variables) > 0:
# raise ValueError("Hidden variables currently not supported for "
Expand Down Expand Up @@ -1331,224 +1347,12 @@ def _get_minmax_lag(links):
from matplotlib import pyplot as plt
import sys

import sklearn
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor


# def lin_f(x): return x
# coeff = .5

# links_coeffs = {0: [((0, -1), 0.5, lin_f)],
# 1: [((1, -1), 0.5, lin_f), ((0, -1), 0.5, lin_f)],
# 2: [((2, -1), 0.5, lin_f), ((1, 0), 0.5, lin_f)]
# }
# T = 1000
# data, nonstat = toys.structural_causal_process(
# links_coeffs, T=T, noises=None, seed=7)
# dataframe = pp.DataFrame(data)

# graph = CausalEffects.get_graph_from_dict(links_coeffs)

# original_graph = np.array([[['', ''],
# ['-->', ''],
# ['-->', ''],
# ['', '']],

# [['<--', ''],
# ['', '-->'],
# ['-->', ''],
# ['-->', '']],

# [['<--', ''],
# ['<--', ''],
# ['', '-->'],
# ['-->', '']],

# [['', ''],
# ['<--', ''],
# ['<--', ''],
# ['', '-->']]], dtype='<U3')
# graph = np.copy(original_graph)

# # Add T <-> Reco and T
# graph[2,3,0] = '+->' ; graph[3,2,0] = '<-+'
# graph[1,3,1] = '<->' #; graph[2,1,0] = '<--'

# added = np.zeros((4, 4, 1), dtype='<U3')
# added[:] = ""
# graph = np.append(graph, added , axis=2)


# X = [(1, 0)]
# Y = [(3, 0)]

# # # Initialize class as `stationary_dag`
# causal_effects = CausalEffects(graph, graph_type='stationary_admg',
# X=X, Y=Y, S=None,
# hidden_variables=None,
# verbosity=0)

# print(causal_effects.get_optimal_set())

# tp.plot_time_series_graph(
# graph = graph,
# save_name='Example_graph_in.pdf',
# # special_nodes=special_nodes,
# # var_names=var_names,
# figsize=(6, 4),
# )

# tp.plot_time_series_graph(
# graph = causal_effects.graph,
# save_name='Example_graph_out.pdf',
# # special_nodes=special_nodes,
# # var_names=var_names,
# figsize=(6, 4),
# )

# causal_effects.fit_wright_effect(dataframe=dataframe,
# # links_coeffs = links_coeffs,
# # mediation = [(1, 0), (1, -1), (1, -2)]
# )

# intervention_data = 1.*np.ones((1, 1))
# y1 = causal_effects.predict_wright_effect(
# intervention_data=intervention_data,
# )

# intervention_data = 0.*np.ones((1, 1))
# y2 = causal_effects.predict_wright_effect(
# intervention_data=intervention_data,
# )

# beta = (y1 - y2)
# print("Causal effect is %.5f" %(beta))

# tp.plot_time_series_graph(
# graph = causal_effects.graph,
# save_name='Example_graph.pdf',
# # special_nodes=special_nodes,
# var_names=var_names,
# figsize=(8, 4),
# )

T = 10000
def lin_f(x): return x

auto_coeff = 0.
coeff = 2.

links = {
0: [((0, -1), auto_coeff, lin_f)],
1: [((1, -1), auto_coeff, lin_f)],
2: [((2, -1), auto_coeff, lin_f), ((0, 0), coeff, lin_f)],
3: [((3, -1), auto_coeff, lin_f)],
}
data, nonstat = toys.structural_causal_process(links, T=T,
noises=None, seed=7)


# # Create some missing values
# data[-10:,:] = 999.
# var_names = range(2)

dataframe = pp.DataFrame(data,
vector_vars={0:[(0,0), (1,0)],
1:[(2,0), (3,0)]}
)

# # Construct expert knowledge graph from links here
aux_links = {0: [(0, -1)],
1: [(1, -1), (0, 0)],
}
# # Use staticmethod to get graph
graph = CausalEffects.get_graph_from_dict(aux_links, tau_max=2)
# graph = np.array([['', '-->'],
# ['<--', '']], dtype='<U3')
graph = np.array([['', '-->'],
['<--', '']], dtype='<U3')

# # We are interested in lagged total effect of X on Y
X = [(0, 0), (0, -1)]
Y = [(1, 0), (1, -1)]

# # Initialize class as `stationary_dag`
causal_effects = CausalEffects(graph, graph_type='stationary_dag',
X=X, Y=Y, S=None,
hidden_variables=None,
causal_effects = Graphs(graph, graph_type='dag', tau_max=0,
verbosity=1)

# print(data)
# # Optimal adjustment set (is used by default)
# # print(causal_effects.get_optimal_set())

# # # Fit causal effect model from observational data
causal_effects.fit_total_effect(
dataframe=dataframe,
# mask_type='y',
estimator=LinearRegression(),
)

# # Fit causal effect model from observational data
# causal_effects.fit_bootstrap_of(
# method='fit_total_effect',
# method_args={'dataframe':dataframe,
# # mask_type='y',
# 'estimator':LinearRegression()
# },
# boot_samples=3,
# boot_blocklength=1,
# seed=5
# )


# Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
lenX = 4 # len(dataframe.vector_vars[X[0][0]])
dox_vals = np.linspace(0., 1., 3)
intervention_data = np.tile(dox_vals.reshape(len(dox_vals), 1), lenX)

intervention_data = np.array([[1., 0., 0., 0.]])

print(intervention_data)

pred_Y = causal_effects.predict_total_effect(
intervention_data=intervention_data)
print(pred_Y, pred_Y.shape)





# # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
# # dox_vals = np.array([1.]) #np.linspace(0., 1., 1)
# intervention_data = np.tile(dox_vals.reshape(len(dox_vals), 1), len(X))
# conf = causal_effects.predict_bootstrap_of(
# method='predict_total_effect',
# method_args={'intervention_data':intervention_data})
# print(conf, conf.shape)



# # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
# # dox_vals = np.array([1.]) #np.linspace(0., 1., 1)
# # intervention_data = dox_vals.reshape(len(dox_vals), len(X))
# # pred_Y = causal_effects.predict_total_effect(
# # intervention_data=intervention_data)
# # print(pred_Y)



# # Fit causal effect model from observational data
# causal_effects.fit_wright_effect(
# dataframe=dataframe,
# # mask_type='y',
# # estimator=LinearRegression(),
# # data_transform=StandardScaler(),
# )

# # # Predict effect of interventions do(X=0.), ..., do(X=1.) in one go
# dox_vals = np.linspace(0., 1., 5)
# intervention_data = dox_vals.reshape(len(dox_vals), len(X))
# pred_Y = causal_effects.predict_wright_effect(
# intervention_data=intervention_data)
# print(pred_Y)

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