Merge pull request #121 from jakobrunge/developer

Developer

environment_py3.yml

@@ -1,12 +1,5 @@
 name: tigramite4_conda
 dependencies:
     - python=3
-    - numpy>=1.14.0
+    - numpy>=1.17.0
     - scipy>=1.3.0
-    - scikit-learn>=0.21.2
-    - matplotlib>=3.1.1
-    - networkx>=2.4
-    - cython>=0.26
-    - torch>=1.7
-    - gpytorch>=1.4
-    - dcor>=0.5.3

setup.py

@@ -66,11 +66,11 @@ def define_extension(extension_name, source_files=None):
 EXTRAS_REQUIRE = {
     "all": [
         "scikit-learn>=0.21",  # Gaussian Process (GP) Regression
-        "matplotlib>=3.0",     # plotting
+        "matplotlib>=3.4.0",     # plotting
         "networkx>=2.4",       # plotting
         "torch>=1.7",          # GPDC torch version
-        "gpytorch>=1.4",       # GPDC torch version
-        "dcor>=0.5.3",         # GPDC
+        "gpytorch>=1.4",       # GPDC gpytorch version
+        "dcor>=0.5.3",         # GPDC distance correlation version
     ]
 }
 
@@ -90,7 +90,7 @@ def define_extension(extension_name, source_files=None):
 # Run the setup
 setup(
     name="tigramite",
-    version="4.2.2.0",
+    version="4.2.2.1",
     packages=["tigramite", "tigramite.independence_tests"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",

tigramite/independence_tests/cmiknn.py

@@ -7,13 +7,12 @@
 from __future__ import print_function
 from scipy import special, stats, spatial
 import numpy as np
-
+import warnings
 from .independence_tests_base import CondIndTest
-
 try:
     from tigramite import tigramite_cython_code
-except:
-    print("Could not import packages for CMIknn and GPDC estimation")
+except Exception as e:
+    warnings.warn(str(e))
 
 
 class CMIknn(CondIndTest):

tigramite/independence_tests/gpdc.py

@@ -6,14 +6,14 @@
 
 from __future__ import print_function
 import numpy as np
-import dcor
-
+import warnings
 from .independence_tests_base import CondIndTest
 
 try:
+    import dcor
     from sklearn import gaussian_process
-except:
-    print("Could not import sklearn for Gaussian process tests")
+except Exception as e:
+    warnings.warn(str(e))
 
 class GaussProcReg():
     r"""Gaussian processes abstract base class.

tigramite/independence_tests/gpdc_torch.py

@@ -4,17 +4,18 @@
 #
 # License: GNU General Public License v3.0
 
-
-
 from __future__ import print_function
+import warnings
 import numpy as np
-import gpytorch
-import torch
-import dcor
 import gc
-
 from .independence_tests_base import CondIndTest
-from .LBFGS import FullBatchLBFGS
+try:
+    import dcor
+    import torch
+    import gpytorch
+    from .LBFGS import FullBatchLBFGS
+except Exception as e:
+    warnings.warn(str(e))
 
 class GaussProcRegTorch():
     r"""Gaussian processes abstract base class.

tigramite/plotting.py

@@ -1354,7 +1354,7 @@ def draw_edge(
             cax_e = pyplot.axes(
                 [
                     0.55,
-                    ax.figbox.bounds[1] + 0.02,
+                    ax.get_subplotspec().get_position(ax.figure).bounds[1] + 0.02,
                     0.4,
                     0.025 + (len(all_links_edge_weights) == 0) * 0.035,
                 ],
@@ -1375,7 +1375,7 @@ def draw_edge(
             # except:
             #     print('no ticks given')
 
-            cb_e.outline.remove()
+            cb_e.outline.clear()
             cax_e.set_xlabel(
                 link_colorbar_label, labelpad=1, fontsize=label_fontsize, zorder=-10
             )
@@ -1442,13 +1442,13 @@ def get_aspect(ax):
             if node_rings[ring]["colorbar"]:
                 # Create colorbars for nodes
                 # cax_n = pyplot.axes([.8 + ring*0.11,
-                # ax.figbox.bounds[1]+0.05, 0.025, 0.35], frameon=False) #
+                # ax.get_subplotspec().get_position(ax.figure).bounds[1]+0.05, 0.025, 0.35], frameon=False) #
                 # setup colorbar axes.
                 # setup colorbar axes.
                 cax_n = pyplot.axes(
                     [
                         0.05,
-                        ax.figbox.bounds[1] + 0.02 + ring * 0.11,
+                        ax.get_subplotspec().get_position(ax.figure).bounds[1] + 0.02 + ring * 0.11,
                         0.4,
                         0.025 + (len(node_rings) == 1) * 0.035,
                     ],
@@ -1466,7 +1466,7 @@ def get_aspect(ax):
                 )
                 # except:
                 #     print ('no ticks given')
-                cb_n.outline.remove()
+                cb_n.outline.clear()
                 # cb_n.set_ticks()
                 cax_n.set_xlabel(
                     node_rings[ring]["label"], labelpad=1, fontsize=label_fontsize

tutorials/tigramite_tutorial_basics.ipynb

@@ -749,6 +749,16 @@
    "execution_count": 18,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/rung_ja/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/gaussian_process/kernels.py:409: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__noise_level is close to the specified lower bound 1e-05. Decreasing the bound and calling fit again may find a better value.\n",
+      "  ConvergenceWarning)\n",
+      "/home/rung_ja/anaconda3/envs/py36/lib/python3.6/site-packages/sklearn/gaussian_process/kernels.py:409: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__noise_level is close to the specified lower bound 1e-05. Decreasing the bound and calling fit again may find a better value.\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -994,7 +1004,7 @@
       "    No conditions of dimension 1 left.\n",
       "\n",
       "    Link ($X^0$ -1) --> $X^2$ (2/2):\n",
-      "    Subset 0: ($X^1$ -2)  gives pval = 0.14700 / val =  0.022\n",
+      "    Subset 0: ($X^1$ -2)  gives pval = 0.14900 / val =  0.022\n",
       "    Non-significance detected.\n",
       "\n",
       "    Sorting parents in decreasing order with \n",