ML_LogisticRegression1.ipynb

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    "import graphlab as gl"
   ]
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      "This non-commercial license of GraphLab Create for academic use is assigned to padhigayatri.official@gmail.com and will expire on January 02, 2019.\n"
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       "<pre>Downloading https://static.turi.com/datasets/regression/yelp-data.csv to C:/Users/User/AppData/Local/Temp/graphlab-User/4312/2d2a4592-6085-48df-a5d9-f8a0404ff5c4.csv</pre>"
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       "Downloading https://static.turi.com/datasets/regression/yelp-data.csv to C:/Users/User/AppData/Local/Temp/graphlab-User/4312/2d2a4592-6085-48df-a5d9-f8a0404ff5c4.csv"
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       "Finished parsing file https://static.turi.com/datasets/regression/yelp-data.csv"
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      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[str,str,str,long,str,str,str,dict,long,long,long,list,str,str,float,float,str,long,long,float,str,str,float,str,long,str,long,long,long,dict]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
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   "source": [
    "# Load the data\n",
    "data =  gl.SFrame('https://static.turi.com/datasets/regression/yelp-data.csv')"
   ]
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   "cell_type": "code",
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   "source": [
    "# Restaurants with rating >=3 are good\n",
    "data['is_good'] = data['stars'] >= 3"
   ]
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   "source": [
    "# Make a train-test split\n",
    "train_data, test_data = data.random_split(0.8)"
   ]
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      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n"
     ]
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     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
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      "text/plain": [
       "Logistic regression:"
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     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
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      "text/html": [
       "<pre>Number of examples          : 164260</pre>"
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       "Number of examples          : 164260"
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     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
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      "text/html": [
       "<pre>Number of feature columns   : 4</pre>"
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       "Number of feature columns   : 4"
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      "text/html": [
       "<pre>Number of unpacked features : 4</pre>"
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       "Number of unpacked features : 4"
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       "<pre>Number of coefficients    : 5</pre>"
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      "text/plain": [
       "Number of coefficients    : 5"
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      "text/html": [
       "<pre>Starting Newton Method</pre>"
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       "Starting Newton Method"
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      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
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      "text/plain": [
       "--------------------------------------------------------"
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       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
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       "+-----------+----------+--------------+-------------------+---------------------+"
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      "text/html": [
       "<pre>| Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
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      "text/plain": [
       "| Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |"
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       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
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       "+-----------+----------+--------------+-------------------+---------------------+"
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       "<pre>| 1         | 2        | 0.092005     | 0.863065          | 0.861949            |</pre>"
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       "<pre>| 2         | 3        | 0.167010     | 0.865889          | 0.865893            |</pre>"
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       "| 2         | 3        | 0.167010     | 0.865889          | 0.865893            |"
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       "<pre>| 3         | 4        | 0.227013     | 0.866206          | 0.867517            |</pre>"
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       "| 3         | 4        | 0.227013     | 0.866206          | 0.867517            |"
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       "<pre>| 4         | 5        | 0.311018     | 0.866255          | 0.866473            |</pre>"
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       "| 4         | 5        | 0.311018     | 0.866255          | 0.866473            |"
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       "<pre>SUCCESS: Optimal solution found.</pre>"
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       "SUCCESS: Optimal solution found."
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       "<pre></pre>"
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   "source": [
    "# Create a model.\n",
    "model = gl.logistic_classifier.create(train_data, target='is_good',\n",
    "                                    features = ['user_avg_stars',\n",
    "                                                'business_avg_stars',\n",
    "                                                'user_review_count',\n",
    "                                                'business_review_count'])"
   ]
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   "execution_count": 6,
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   "source": [
    "# Save predictions (probability estimates) to an SArray\n",
    "predictions = model.classify(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "# Evaluate the model and save the results into a dictionary\n",
    "results = model.evaluate(test_data)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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     "text": [
      "+-------+----------------+\n",
      "| class |  probability   |\n",
      "+-------+----------------+\n",
      "|   1   | 0.935375021545 |\n",
      "|   1   | 0.980682157565 |\n",
      "|   1   | 0.949946481437 |\n",
      "|   1   | 0.703877419017 |\n",
      "|   1   | 0.900165764219 |\n",
      "|   1   | 0.987947227111 |\n",
      "|   1   | 0.990600826761 |\n",
      "|   1   | 0.997303391469 |\n",
      "|   1   | 0.951978903198 |\n",
      "|   1   | 0.933727346237 |\n",
      "+-------+----------------+\n",
      "[42999 rows x 2 columns]\n",
      "Note: Only the head of the SFrame is printed.\n",
      "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n"
     ]
    }
   ],
   "source": [
    "predictions = model.classify(test_data)\n",
    "print predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "pred_class = model.predict(test_data, output_type = \"class\")          # Class\n",
    "pred_prob_one = model.predict(test_data, output_type = 'probability') # Probability\n",
    "pred_margin = model.predict(test_data, output_type = \"margin\")        # Margins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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     "text": [
      "Accuracy         : 0.868020186516\n",
      "Confusion Matrix : \n",
      "+--------------+-----------------+-------+\n",
      "| target_label | predicted_label | count |\n",
      "+--------------+-----------------+-------+\n",
      "|      1       |        0        |  890  |\n",
      "|      0       |        0        |  2329 |\n",
      "|      0       |        1        |  4785 |\n",
      "|      1       |        1        | 34995 |\n",
      "+--------------+-----------------+-------+\n",
      "[4 rows x 3 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(test_data)\n",
    "print \"Accuracy         : %s\" % result['accuracy']\n",
    "print \"Confusion Matrix : \\n%s\" % result['confusion_matrix']"
   ]
  },
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   "execution_count": null,
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