graduated!

.ipynb_checkpoints/ML Practice-checkpoint.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cancer.keys(): /ndict_keys(['feature_names', 'DESCR', 'data', 'target', 'target_names'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer, load_boston\n",
+    "\n",
+    "cancer = load_breast_cancer()\n",
+    "print('cancer.keys(): /n{}'.format(cancer.keys()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of cancer is (569, 30)\n",
+      "Sample counts per class: \n",
+      "{'benign': 357, 'malignant': 212}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('shape of cancer is {}'.format(cancer.data.shape))\n",
+    "print('Sample counts per class: \\n{}'.format({n: v for n,v in zip(cancer.target_names, np.bincount(cancer.target))}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(569,)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cancer.target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#custom test train split function\n",
+    "def test_train_split(ndmatrix, test_size = .3):\n",
+    "    import numpy as np\n",
+    "    rows = ndmatrix.data.shape[0]\n",
+    "    rand_inx = np.random.choice(range(rows), rows, False) #another strategy, randomize everything, then just split by position\n",
+    "    data = ndmatrix.data[rand_inx]\n",
+    "    target = ndmatrix.target[rand_inx]\n",
+    "    split_inx = int(rows*test_size)\n",
+    "    X_train, X_test = data[split_inx:], data[:split_inx]\n",
+    "    y_train, y_test = target[split_inx:], target[:split_inx]\n",
+    "    return X_train, y_train, X_test, y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVC scores: \n",
+      "0.6235294117647059\n",
+      "Naive Bayes scores: \n",
+      "0.9176470588235294\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "svc = SVC(C=.001)\n",
+    "svc.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"SVC scores: \\n{}\".format(svc.score(X_test, y_test)))\n",
+    "\n",
+    "X_train, y_train, X_test, y_test = test_train_split(cancer)\n",
+    "nb = GaussianNB()\n",
+    "nb.fit(X_train, y_train)\n",
+    "print(\"Naive Bayes scores: \\n{}\".format(nb.score(X_test, y_test)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "399 170\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(X_train),len(X_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Deep Learning/credentials.csv

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+User name,Password,Access key ID,Secret access key,Console login link
+ctivan,TvxCp$r#NEGA,AKIAIXOLXICVHP6X7VSA,XdFzw+qWPYwOqiCAwKDRXjBtIEmkSCxf6rrDEH+S,https://137009966217.signin.aws.amazon.com/console
+,,,,
+,,,,
+Connect to your instance: ssh -i /home/nodal/.ssh/aws-key-fast-ai.pem [email protected],,,,

Deep Learning/fast-ai-commands.txt

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+# Connect to your instance:
+ssh -i /home/nodal/.ssh/aws-key-fast-ai.pem [email protected]
+# Stop your instance: :
+aws ec2 stop-instances --instance-ids i-0532431a82b095705
+# Start your instance:
+aws ec2 start-instances --instance-ids i-0532431a82b095705
+# Reboot your instance:
+aws ec2 reboot-instances --instance-ids i-0532431a82b095705
+export instanceId=i-0532431a82b095705
+export subnetId=subnet-9b70e0d3
+export securityGroupId=sg-8b144bf6
+export instanceUrl=ec2-54-69-62-69.us-west-2.compute.amazonaws.com
+export routeTableId=rtb-19f49360
+export name=fast-ai
+export vpcId=vpc-9c527cfa
+export internetGatewayId=igw-ff9ea198
+export subnetId=subnet-9b70e0d3
+export allocAddr=eipalloc-4e86e773
+export assocId=eipassoc-5fee7c60
+export routeTableAssoc=rtbassoc-f618108e

Deep Learning/fast-ai-remove.sh

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+#!/bin/bash
+aws ec2 disassociate-address --association-id eipassoc-5fee7c60
+aws ec2 release-address --allocation-id eipalloc-4e86e773
+aws ec2 terminate-instances --instance-ids i-0532431a82b095705
+aws ec2 wait instance-terminated --instance-ids i-0532431a82b095705
+aws ec2 delete-security-group --group-id sg-8b144bf6
+aws ec2 disassociate-route-table --association-id rtbassoc-f618108e
+aws ec2 delete-route-table --route-table-id rtb-19f49360
+aws ec2 detach-internet-gateway --internet-gateway-id igw-ff9ea198 --vpc-id vpc-9c527cfa
+aws ec2 delete-internet-gateway --internet-gateway-id igw-ff9ea198
+aws ec2 delete-subnet --subnet-id subnet-9b70e0d3
+aws ec2 delete-vpc --vpc-id vpc-9c527cfa
+echo If you want to delete the key-pair, please do it manually.