keras_work.ipynb

{
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   "cell_type": "code",
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     "end_time": "2018-06-19T17:57:52.459787Z",
     "start_time": "2018-06-19T17:57:31.983384Z"
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     "name": "stderr",
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     "text": [
      "C:\\Users\\MY\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:57:52.832135Z",
     "start_time": "2018-06-19T17:57:52.463789Z"
    }
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('final_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:57:52.899131Z",
     "start_time": "2018-06-19T17:57:52.836133Z"
    }
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-Jan-11</td>\n",
       "      <td>-0.8934</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.082</td>\n",
       "      <td>119.49</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4-Jan-11</td>\n",
       "      <td>-0.6996</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.060</td>\n",
       "      <td>121.87</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5-Jan-11</td>\n",
       "      <td>-0.9442</td>\n",
       "      <td>0.319</td>\n",
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       "      <th>3</th>\n",
       "      <td>6-Jan-11</td>\n",
       "      <td>-0.7906</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.094</td>\n",
       "      <td>124.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7-Jan-11</td>\n",
       "      <td>-0.5859</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.094</td>\n",
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      ],
      "text/plain": [
       "       Date  compound    neg    neu    pos   Close\n",
       "0  3-Jan-11   -0.8934  0.172  0.746  0.082  119.49\n",
       "1  4-Jan-11   -0.6996  0.133  0.806  0.060  121.87\n",
       "2  5-Jan-11   -0.9442  0.319  0.638  0.044  124.93\n",
       "3  6-Jan-11   -0.7906  0.179  0.726  0.094  124.98\n",
       "4  7-Jan-11   -0.5859  0.140  0.766  0.094  124.99"
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     "execution_count": 3,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "end_time": "2018-06-19T17:57:52.963130Z",
     "start_time": "2018-06-19T17:57:52.907132Z"
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   "source": [
    "X=df[['compound','neg','neu','pos']]\n",
    "# X=df[['neg','neu','pos']]\n",
    "# Y=df[['Close']]/400     #normalize it\n",
    "Y=df[['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:57:53.080831Z",
     "start_time": "2018-06-19T17:57:52.969137Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:57:53.174977Z",
     "start_time": "2018-06-19T17:57:53.084833Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "min_max_scalar=preprocessing.MinMaxScaler()\n",
    "X_train=min_max_scalar.fit_transform(X_train)\n",
    "X_test=min_max_scalar.fit_transform(X_test)\n",
    "Y_train=min_max_scalar.fit_transform(Y_train)\n",
    "Y_test=min_max_scalar.fit_transform(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:57:53.282361Z",
     "start_time": "2018-06-19T17:57:53.178973Z"
    }
   },
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "# scalar=StandardScaler()\n",
    "# scalar.fit(X_train)\n",
    "# X_train=scalar.transform(X_train)\n",
    "# X_test=scalar.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:55.504163Z",
     "start_time": "2018-06-19T17:57:53.286358Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MY\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import losses\n",
    "from keras import activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:55.517160Z",
     "start_time": "2018-06-19T17:58:55.509162Z"
    }
   },
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:56.480189Z",
     "start_time": "2018-06-19T17:58:55.524163Z"
    }
   },
   "outputs": [],
   "source": [
    "model.add(Dense(4,activation=activations.sigmoid,input_shape=(4,)))\n",
    "# model.add(Dense(3,activation='relu',input_shape=(3,)))\n",
    "model.add(Dense(100,activation=activations.sigmoid))\n",
    "model.add(Dense(100,activation=activations.sigmoid))\n",
    "model.add(Dense(100,activation=activations.sigmoid))\n",
    "model.add(Dense(100,activation=activations.sigmoid))\n",
    "model.add(Dense(100,activation=activations.sigmoid))\n",
    "model.add(Dense(1,activation=activations.sigmoid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:56.660159Z",
     "start_time": "2018-06-19T17:58:56.484182Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss=losses.mean_absolute_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:56.673154Z",
     "start_time": "2018-06-19T17:58:56.665157Z"
    }
   },
   "outputs": [],
   "source": [
    "# while 1:\n",
    "#     model.fit(X_train,Y_train,epochs=500)\n",
    "#     y_pred=model.predict(X_test)\n",
    "#     falto=r2_score(Y_test,y_pred)\n",
    "#     print(falto)\n",
    "#     if falto >= 70.0:\n",
    "#         break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:59.756572Z",
     "start_time": "2018-06-19T17:58:56.678156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      " - 2s - loss: 0.2012\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x211e0173a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:59.884566Z",
     "start_time": "2018-06-19T17:58:59.761571Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:58:59.900567Z",
     "start_time": "2018-06-19T17:58:59.890569Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.00386725945672195\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:00.010564Z",
     "start_time": "2018-06-19T17:58:59.908571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-11.1517567590531"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-11.1517567590531"
   ]
  },
  {
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    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:00.143256Z",
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     "metadata": {},
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  {
   "cell_type": "code",
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   "metadata": {
    "ExecuteTime": {
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     "start_time": "2018-06-19T17:59:00.147256Z"
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     "execution_count": 18,
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2018-06-19T17:59:00.213256Z"
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     "name": "stdout",
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     "text": [
      "1236/1236 [==============================] - 0s 156us/step\n"
     ]
    },
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     "data": {
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   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "610/610 [==============================] - 0s 84us/step\n"
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   "metadata": {
    "ExecuteTime": {
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     "text": [
      "610/610 [==============================] - 0s 113us/step\n"
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     "start_time": "2018-06-19T17:59:00.707249Z"
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    {
     "data": {
      "text/plain": [
       "[array([[-0.39439338,  0.7394769 ,  0.5976062 ,  0.10832803],\n",
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       "       dtype=float32),\n",
       " array([ 0.00716783,  0.00444512, -0.00157858,  0.00503871], dtype=float32)]"
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       "       [-0.53737074,  0.37399262, -0.07217079,  0.05122531],\n",
       "       [-0.14279613, -0.00128876, -0.2621399 , -0.40994066],\n",
       "       [-0.28114653, -0.8427895 , -0.0926476 , -0.30379266]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[0].get_weights()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:01.042163Z",
     "start_time": "2018-06-19T17:59:00.951165Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00716783,  0.00444512, -0.00157858,  0.00503871], dtype=float32)"
      ]
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     "execution_count": 24,
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     "output_type": "execute_result"
    }
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   "source": [
    "model.layers[0].get_weights()[1]"
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  {
   "cell_type": "code",
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    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:01.237157Z",
     "start_time": "2018-06-19T17:59:01.049161Z"
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       "       dtype=float32)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[1].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:01.312158Z",
     "start_time": "2018-06-19T17:59:01.257157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.layers.core.Dense at 0x211e0185630>,\n",
       " <keras.layers.core.Dense at 0x211e01855f8>,\n",
       " <keras.layers.core.Dense at 0x211e01858d0>,\n",
       " <keras.layers.core.Dense at 0x211e019dbe0>,\n",
       " <keras.layers.core.Dense at 0x211e01ba860>,\n",
       " <keras.layers.core.Dense at 0x211e01f4780>,\n",
       " <keras.layers.core.Dense at 0x211e020cd30>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "model.layers"
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  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:01.449161Z",
     "start_time": "2018-06-19T17:59:01.318160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
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     "execution_count": 27,
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     "output_type": "execute_result"
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   "source": [
    "len(model.layers[2].get_weights()[0])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:24.545167Z",
     "start_time": "2018-06-19T17:59:24.517167Z"
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   "outputs": [
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      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_max_scalar.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-19T17:59:34.677482Z",
     "start_time": "2018-06-19T17:59:34.650484Z"
    }
   },
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