Data_exploration.ipynb

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     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "df_data = pd.read_pickle('./saved_variables/df_data')\n",
    "df_X, df_y, df_data = pd.read_pickle('saved_variables/df_X_y_data')\n",
    "X_train, X_test, y_train, y_test = pd.read_pickle('./saved_variables/X_y_train_test')\n",
    "feature_names, class_names = pd.read_pickle('saved_variables/feature_class_names')"
   ]
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     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'diagnosis_num', 'radius_mean', 'texture_mean',\n",
       "       'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean',\n",
       "       'concavity_mean', 'concave_points_mean', 'symmetry_mean',\n",
       "       'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se',\n",
       "       'area_se', 'smoothness_se', 'compactness_se', 'concavity_se',\n",
       "       'concave_points_se', 'symmetry_se', 'fractal_dimension_se',\n",
       "       'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',\n",
       "       'smoothness_worst', 'compactness_worst', 'concavity_worst',\n",
       "       'concave_points_worst', 'symmetry_worst', 'fractal_dimension_worst'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data.columns"
   ]
  },
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a68b611c-e6f0-481c-80dd-95c623e3173a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "fig = px.scatter_3d(df_data, x='area_mean', y='compactness_mean', z='texture_mean',\n",
    "              color='diagnosis', symbol='diagnosis', opacity=1, width=900) #size=''\n",
    "\n",
    "# tight layout\n",
    "#fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Editble Datatable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parrallel Coordinate Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_dimensions = ['body-style', 'drive-wheels', 'fuel-type']\n",
    "\n",
    "dimensions = [dict(values=cars_df[label], label=label) for label in categorical_dimensions]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('f2ea159e-308f-4fe2-8fe1-bb71e08021a8');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/bcdunbar/datasets/master/parcoords_data.csv\")\n",
    "\n",
    "fig = go.Figure(data=\n",
    "    go.Parcoords(\n",
    "        line = dict(color = df['colorVal'],\n",
    "                   colorscale = 'Electric',\n",
    "                   showscale = True,\n",
    "                   cmin = -4000,\n",
    "                   cmax = -100),\n",
    "        dimensions = list([\n",
    "            dict(range = [32000,227900],\n",
    "                 constraintrange = [100000,150000],\n",
    "                 label = \"Block Height\", values = df['blockHeight']),\n",
    "            dict(range = [0,700000],\n",
    "                 label = 'Block Width', values = df['blockWidth']),\n",
    "            dict(tickvals = [0,0.5,1,2,3],\n",
    "                 ticktext = ['A','AB','B','Y','Z'],\n",
    "                 label = 'Cyclinder Material', values = df['cycMaterial']),\n",
    "            dict(range = [-1,4],\n",
    "                 tickvals = [0,1,2,3],\n",
    "                 label = 'Block Material', values = df['blockMaterial']),\n",
    "            dict(range = [134,3154],\n",
    "                 visible = True,\n",
    "                 label = 'Total Weight', values = df['totalWeight']),\n",
    "            dict(range = [9,19984],\n",
    "                 label = 'Assembly Penalty Wt', values = df['assemblyPW']),\n",
    "            dict(range = [49000,568000],\n",
    "                 label = 'Height st Width', values = df['HstW'])])\n",
    "    )\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11fd97bcff43452298bccbd93b8504c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(ToggleButtons(description='Brush Color:', index=1, options=('None', 'Red', 'Blue'), value='Red'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "import ipywidgets as widgets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "cars_df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/imports-85.csv')\n",
    "\n",
    "# Build parcats dimensions\n",
    "categorical_dimensions = ['body-style', 'drive-wheels', 'fuel-type']\n",
    "\n",
    "dimensions = [dict(values=cars_df[label], label=label) for label in categorical_dimensions]\n",
    "\n",
    "# Build colorscale\n",
    "color = np.zeros(len(cars_df), dtype='uint8')\n",
    "colorscale = [[0, 'gray'], [0.33, 'gray'],\n",
    "              [0.33, 'firebrick'], [0.66, 'firebrick'],\n",
    "              [0.66, 'blue'], [1.0, 'blue']]\n",
    "cmin = -0.5\n",
    "cmax = 2.5\n",
    "\n",
    "# Build figure as FigureWidget\n",
    "fig = go.FigureWidget(\n",
    "    data=[go.Scatter(x=cars_df.horsepower, y=cars_df['highway-mpg'],\n",
    "                marker={'color': color, 'cmin': cmin, 'cmax': cmax,\n",
    "                        'colorscale': colorscale, 'showscale': True,\n",
    "                        'colorbar': {'tickvals': [0, 1, 2], 'ticktext': ['None', 'Red', 'Blue']}},\n",
    "                     mode='markers'),\n",
    "\n",
    "      go.Parcats(domain={'y': [0, 0.4]}, dimensions=dimensions,\n",
    "                   line={'colorscale': colorscale, 'cmin': cmin,\n",
    "                   'cmax': cmax, 'color': color, 'shape': 'hspline'})]\n",
    ")\n",
    "\n",
    "fig.update_layout(height=800, xaxis={'title': 'Horsepower'},\n",
    "                  yaxis={'title': 'MPG', 'domain': [0.6, 1]},\n",
    "                  dragmode='lasso', hovermode='closest')\n",
    "\n",
    "# Build color selection widget\n",
    "color_toggle = widgets.ToggleButtons(\n",
    "    options=['None', 'Red', 'Blue'],\n",
    "    index=1, description='Brush Color:', disabled=False)\n",
    "\n",
    "# Update color callback\n",
    "def update_color(trace, points, state):\n",
    "    # Compute new color array\n",
    "    new_color = np.array(fig.data[0].marker.color)\n",
    "    new_color[points.point_inds] = color_toggle.index\n",
    "\n",
    "    with fig.batch_update():\n",
    "        # Update scatter color\n",
    "        fig.data[0].marker.color = new_color\n",
    "\n",
    "        # Update parcats colors\n",
    "        fig.data[1].line.color = new_color\n",
    "\n",
    "# Register callback on scatter selection...\n",
    "fig.data[0].on_selection(update_color)\n",
    "# and parcats click\n",
    "fig.data[1].on_click(update_color)\n",
    "\n",
    "# Display figure\n",
    "widgets.VBox([color_toggle, fig])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial Dependence Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\blake\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning:\n",
      "\n",
      "The sklearn.datasets.california_housing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "\n",
    "#from __future__ import print_function\n",
    "#print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble.partial_dependence import plot_partial_dependence\n",
    "from sklearn.ensemble.partial_dependence import partial_dependence\n",
    "from sklearn.datasets.california_housing import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = pd.read_pickle('./saved_variables/X_y_train_test')\n",
    "df_X, df_y, df_data = pd.read_pickle('saved_variables/df_X_y_data')\n",
    "feature_names, class_names = pd.read_pickle('saved_variables/feature_class_names')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matplotlib_to_plotly(cmap, pl_entries):\n",
    "    h = 1.0/(pl_entries-1)\n",
    "    pl_colorscale = []\n",
    "    \n",
    "    for k in range(pl_entries):\n",
    "        C = map(np.uint8, np.array(cmap(k*h)[:3])*255)\n",
    "        pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])\n",
    "        \n",
    "    return pl_colorscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training GBRT...\n",
      " done.\n",
      "Convenience plot with ``partial_dependence_plots``\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\blake\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning:\n",
      "\n",
      "Function plot_partial_dependence is deprecated; The function ensemble.plot_partial_dependence has been deprecated in favour of sklearn.inspection.plot_partial_dependence in  0.21 and will be removed in 0.23.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom 3d plot via ``partial_dependence``\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\blake\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning:\n",
      "\n",
      "Function partial_dependence is deprecated; The function ensemble.partial_dependence has been deprecated in favour of inspection.partial_dependence in 0.21 and will be removed in 0.23.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cal_housing = fetch_california_housing()\n",
    "\n",
    "## split 80/20 train-test\n",
    "#X_train, X_test, y_train, y_test = train_test_split(cal_housing.data,\n",
    "#                                                    cal_housing.target,\n",
    "#                                                    test_size=0.2,\n",
    "#                                                    random_state=1)\n",
    "names = feature_names\n",
    "\n",
    "print(\"Training GBRT...\")\n",
    "clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
    "                                learning_rate=0.1, loss='huber',\n",
    "                                random_state=1)\n",
    "clf.fit(X_train, y_train)\n",
    "print(\" done.\")\n",
    "\n",
    "print('Convenience plot with ``partial_dependence_plots``')\n",
    "\n",
    "features = [0, 5, 1, 2, (5, 1)]\n",
    "fig, axs = plot_partial_dependence(clf, X_train, features,\n",
    "                                   feature_names=names,\n",
    "                                   n_jobs=3, grid_resolution=50)\n",
    "fig.suptitle('Partial dependence of house value on nonlocation features\\n'\n",
    "             'for the California housing dataset')\n",
    "print('Custom 3d plot via ``partial_dependence``')\n",
    "fig = plt.figure()\n",
    "\n",
    "target_feature = (1, 5)\n",
    "pdp, axes = partial_dependence(clf, target_feature,\n",
    "                               X=X_train, grid_resolution=50)\n",
    "XX, YY = np.meshgrid(axes[0], axes[1])\n",
    "Z = pdp[0].reshape(list(map(np.size, axes))).T\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'map' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-4c5a15908ff9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m surf = go.Surface(x=XX, y=YY, z=Z,\n\u001b[1;32m----> 2\u001b[1;33m                   \u001b[0mcolorscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmatplotlib_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBuPu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m                  )\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m layout = go.Layout(title='Partial dependence of house value on median age and '\n",
      "\u001b[1;32m<ipython-input-9-cace071eaf6a>\u001b[0m in \u001b[0;36mmatplotlib_to_plotly\u001b[1;34m(cmap, pl_entries)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpl_entries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m255\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[0mpl_colorscale\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rgb'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mpl_colorscale\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'map' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "surf = go.Surface(x=XX, y=YY, z=Z,\n",
    "                  colorscale=matplotlib_to_plotly(plt.cm.BuPu, 10),\n",
    "                 )\n",
    "\n",
    "layout = go.Layout(title='Partial dependence of house value on median age and '\n",
    "                          'average occupancy',\n",
    "                   scene=dict(xaxis=dict(title=names[target_feature[0]],\n",
    "                                         showticklabels=False),\n",
    "                              yaxis=dict(title=names[target_feature[1]],\n",
    "                                         showticklabels=False),\n",
    "                              zaxis=dict(title='Partial dependence',\n",
    "                                         showticklabels=False))\n",
    "                   )\n",
    "fig = go.Figure(data = [surf], layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "py.iplot(fig)"
   ]
  }
 ],
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