Merge pull request #90 from arangoml/release_0.0.6.8.6

Changed pip install from testpypi to pypi.
arangoml · Feb 17, 2020 · 4569b74 · 4569b74
2 parents 93c8987 + 224d1b4
commit 4569b74
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diff --git a/examples/Arangopipe_Feature_Example_ext1.ipynb b/examples/Arangopipe_Feature_Example_ext1.ipynb
@@ -1,12 +1,4 @@
 {
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
-  "language_info": {
-   "name": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
  "cells": [
   {
    "cell_type": "markdown",
@@ -24,30 +16,30 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "!pip install python-arango\n",
-    "!pip install -i https://test.pypi.org/simple/ arangopipe\n",
+    "!pip install arangopipe==0.0.6.8.6\n",
     "!pip install pandas PyYAML==5.1.1 sklearn2\n",
     "!pip install jsonpickle\n",
     "!pip install seaborn\n",
     "!pip install dtreeviz\n"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "data_url = \"https://raw.githubusercontent.com/arangoml/arangopipe/arangopipe_examples/examples/data/cal_housing.csv\"\n",
     "df = pd.read_csv(data_url, error_bad_lines=False)\n",
     "df.head()"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -69,7 +61,9 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from sklearn import linear_model\n",
     "df['medianHouseValue'] = df['medianHouseValue'].apply(np.log)\n",
@@ -98,22 +92,20 @@
     "        \n",
     "    if index % 100 == 0:\n",
     "        print('Completed estimating %4d points in the dataset' % (index))"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "Xm = df[preds].values\n",
     "Ym = df['medianHouseValue'].values\n",
     "clf_0 = linear_model.Lasso(alpha=0.001, max_iter = 10000)\n",
     "clf_0.fit(Xm, Ym)\n",
     "Yhat_m = clf_0.predict(Xm)"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -124,15 +116,15 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "# see section 2.2 from https://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf\n",
     "# see https://web.engr.oregonstate.edu/~tgd/classes/534/slides/part9.pdf\n",
     "Expval_at_i = { i : np.mean(np.array(bootstrap_Yest[i])) for i in range(df.shape[0])}\n",
     "bias_at_i = {i : Expval_at_i[i] - Yhat_m[i] for i in range(df.shape[0])}\n"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -143,7 +135,9 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
@@ -152,9 +146,7 @@
     "bias_values = [bias for (pt, bias) in bias_at_i.items()]\n",
     "sns.kdeplot(bias_values)\n",
     "plt.grid(True)"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -168,14 +160,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "cluster_labels = KMeans(n_clusters=5, random_state=0).fit_predict(Xm)\n",
     "cluster_labels.shape"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
@@ -186,19 +178,21 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "df_bias = pd.DataFrame(Xm)\n",
     "df_bias['cluster'] = cluster_labels\n",
     "df_bias['bias'] = bias_values\n",
     "df_bias.groupby('cluster')['bias'].agg([np.mean, np.size])"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from arangopipe.arangopipe_storage.arangopipe_api import ArangoPipe\n",
     "from arangopipe.arangopipe_storage.arangopipe_admin_api import ArangoPipeAdmin\n",
@@ -250,9 +244,15 @@
     "                    \"tag\": \"Housing-Price-Hyperopt-Experiment\",\\\n",
     "                    \"project\": \"Housing Price Estimation Project\"}\n",
     "ap.log_run(run_info)"
-   ],
-   "execution_count": null,
-   "outputs": []
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
   }
- ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
 }