Delete unused imports

NeuroDataDesign · Dec 20, 2019 · 2285068 · 2285068
1 parent 880dcb1
commit 2285068
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Showing 2 changed files with 2 additions and 16 deletions.
diff --git a/examples/model_selection/RF_vs_SPORF_random_search.ipynb b/examples/model_selection/RF_vs_SPORF_random_search.ipynb
@@ -34,7 +34,6 @@
     "print(__doc__)\n",
     "\n",
     "from sklearn.model_selection import RandomizedSearchCV\n",
-    "from sklearn.model_selection import GridSearchCV\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -44,16 +43,14 @@
     "from sklearn.datasets import fetch_openml\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn import metrics\n",
-    "from warnings import simplefilter\n",
     "\n",
+    "from warnings import simplefilter\n",
     "simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "from warnings import simplefilter\n",
-    "\n",
     "simplefilter(action=\"ignore\", category=FutureWarning)\n",
     "\n",
     "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -303,13 +300,6 @@
     "\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

diff --git a/examples/model_selection/RF_vs_SPORF_random_search.py b/examples/model_selection/RF_vs_SPORF_random_search.py
@@ -14,9 +14,7 @@
 """
 print(__doc__)
 
-from myconfig import api_key
 from sklearn.model_selection import RandomizedSearchCV
-from sklearn.model_selection import GridSearchCV
 
 import pandas as pd
 import numpy as np
@@ -27,14 +25,12 @@
 from sklearn.model_selection import train_test_split
 from sklearn import metrics
 from warnings import simplefilter
-
 simplefilter(action="ignore", category=FutureWarning)
 from warnings import simplefilter
 simplefilter(action="ignore", category=FutureWarning)
 
 import matplotlib
 import matplotlib.pyplot as plt
-import numpy as np
 
 
 def hyperparameter_optimization_random(X, y, *argv):