HW3 ready for submission

hw3/donors_choose.ipynb

@@ -11,13 +11,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "from scipy import stats\n",
     "import pipe_lib as pl\n",
     "import donation_analysis as don"
    ]
@@ -1251,8 +1252,24 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below is how I convert scores to percentile rankings in my evaluate functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best =list(stats.rankdata(best, 'average')/len(best))"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {

hw3/rayids_mlhelpers.py

@@ -18,9 +18,9 @@ import random
 from scipy import optimize
 import time
 import seaborn as sns
-from mlfunctions import *
+from rayids_mlhelpers import *
 from sklearn.metrics import roc_curve, auc, classification_report, confusion_matrix
-from sklearn import preprocessing, model_selection, svm, metrics, tree, decomposition, svm, grid_seach
+from sklearn import preprocessing, model_selection, svm, metrics, tree, decomposition, svm
 from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, AdaBoostClassifier
 from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier, OrthogonalMatchingPursuit, RandomizedLogisticRegression
 from sklearn.neighbors.nearest_centroid import NearestCentroid
@@ -28,7 +28,6 @@ from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.model_selection import train_test_split
-from sklearn.model_selection import ParameterGrid
 from sklearn.metrics import *
 from sklearn.preprocessing import StandardScaler
 import itertools