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lccambiaghi/6.3 Visualizing coefficients.ipynb

Created December 11, 2018 21:38
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gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n",
"/data/home/lca153/udf_deep_learning\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%cd '/home/lca153/udf_deep_learning/'"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from datetime import timedelta\n",
"\n",
"from scripts.arx_preprocessor import Preprocessor\n",
"from scripts.arx import ARX\n",
"\n",
"from folium import Map, CircleMarker, PolyLine, FeatureGroup, LayerControl\n",
"from folium.plugins import FeatureGroupSubGroup"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"paths = {'bookings_df_path': 'data/focus_full.feather',\n",
" 'holidays_df_path': 'data/holidays_matrix.h5',\n",
" 'seasonal_df_path': 'data/seasonal_matrix.h5'}\n",
"pp_pars = paths.copy()\n",
"\n",
"ar_order = 4\n",
"n_dfus = 20\n",
"horizon = pp_pars['forecast_horizon'] = 4\n",
"\n",
"pp = Preprocessor(**pp_pars)\n",
"varx = ARX(pp, lags_order=ar_order)\n",
"x, Y = varx.get_X_Y()\n",
"top = list(pp.Y.mean().sort_values(ascending=False).index)[:n_dfus]\n",
"\n",
"test_weeks = 52\n",
"start_test = x.index[-1] - timedelta(weeks=test_weeks + 1)\n",
"Y_test = Y[start_test:]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# start with coeff_df: id=DFU, coef=coef\n",
"enet_h4 = np.load('results/enet_ev/r95_h4.npy').item()\n",
"enet_h4 = pd.DataFrame(enet_h4)\n",
"top = list(enet_h4.transpose().index)\n",
"enet_h4_coefs = enet_h4.transpose()['coef']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Coefficients data"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [],
"source": [
"i = 6\n",
"dfu_id = top[i]"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>coef</th>\n",
" <th>abs_coef</th>\n",
" <th>lopfi</th>\n",
" <th>dipla</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>CNYAT_GBFXS_PP_DRY _40_-1</th>\n",
" <td>3.667393</td>\n",
" <td>3.667393</td>\n",
" <td>CNYAT</td>\n",
" <td>GBFXS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CNYAT_NLROT_PP_DRY _40_-1</th>\n",
" <td>0.610678</td>\n",
" <td>0.610678</td>\n",
" <td>CNYAT</td>\n",
" <td>NLROT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CNYAT_GBFXS_PP_DRY _40_-3</th>\n",
" <td>0.343879</td>\n",
" <td>0.343879</td>\n",
" <td>CNYAT</td>\n",
" <td>GBFXS</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" coef abs_coef lopfi dipla\n",
"CNYAT_GBFXS_PP_DRY _40_-1 3.667393 3.667393 CNYAT GBFXS\n",
"CNYAT_NLROT_PP_DRY _40_-1 0.610678 0.610678 CNYAT NLROT\n",
"CNYAT_GBFXS_PP_DRY _40_-3 0.343879 0.343879 CNYAT GBFXS"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"en_coefs = enet_h4_coefs[i]\n",
"en_selected = np.nonzero(en_coefs)[0]\n",
"en_coefs_df = pd.DataFrame(data=en_coefs[en_selected],index=x.columns[en_selected], columns=['coef'])\n",
"en_coefs_df['abs_coef'] = np.abs(en_coefs_df['coef'])\n",
"en_coefs_df = en_coefs_df.sort_values(by='abs_coef', ascending=False).head(3)\n",
"\n",
"# From the ID, extract port of origin and port of destination\n",
"en_coefs_df['lopfi'] = list(map(lambda s: s.split('_')[0], en_coefs_df.index))\n",
"en_coefs_df['dipla'] = list(map(lambda s: s.split('_')[1], en_coefs_df.index))\n",
"en_coefs_df"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lon</th>\n",
" <th>lat</th>\n",
" </tr>\n",
" <tr>\n",
" <th>port</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AEAMN</th>\n",
" <td>55.421800</td>\n",
" <td>25.390600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AEAUH</th>\n",
" <td>54.359300</td>\n",
" <td>24.453100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AEDXB</th>\n",
" <td>55.399333</td>\n",
" <td>25.166666</td>\n",
" </tr>\n",
" </tbody>\n",
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"text/plain": [
" lon lat\n",
"port \n",
"AEAMN 55.421800 25.390600\n",
"AEAUH 54.359300 24.453100\n",
"AEDXB 55.399333 25.166666"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# join with ports and get lat and lon\n",
"ports = pd.read_csv('data/ports.csv', header=None)\n",
"ports.columns = ['port', 'lon', 'lat']\n",
"ports.set_index('port', inplace=True); ports.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [],
"source": [
"en_coefs_df = en_coefs_df.join(ports, on='lopfi')\n",
"en_coefs_df.rename(columns={\"lon\": \"lopfi_lon\", \"lat\": \"lopfi_lat\"}, inplace=True)\n",
"en_coefs_df = en_coefs_df.join(ports, on='dipla')\n",
"en_coefs_df.rename(columns={\"lon\": \"dipla_lon\", \"lat\": \"dipla_lat\"}, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Map"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [],
"source": [
"dfu_lopfi = dfu_id.split('_')[0]\n",
"dfu_dipla = dfu_id.split('_')[1]\n",
"\n",
"dfu_lopfi_lat = ports.loc[dfu_lopfi]['lat']\n",
"dfu_lopfi_lon = ports.loc[dfu_lopfi]['lon']\n",
"\n",
"dfu_dipla_lat = ports.loc[dfu_dipla]['lat']\n",
"dfu_dipla_lon = ports.loc[dfu_dipla]['lon']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = folium.Map(location=[30, 0],\n",
" zoom_start=2, tiles=\"CartoDB dark_matter\")\n",
"\n",
"lopfi = folium.CircleMarker(location=[dfu_lopfi_lat, dfu_lopfi_lon],\n",
" popup=f'LOPFI:{dfu_lopfi}', color='red', fill=True).add_to(m)\n",
"dipla = folium.CircleMarker(location=[dfu_dipla_lat, dfu_dipla_lon], \n",
" popup=f'DIPLA:{dfu_dipla}', color='red', fill=True).add_to(m)\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add coefficients edges"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"colors = ['blue', 'lightblue', 'gray']\n",
"\n",
"for coef_id, row in en_coefs_df.iterrows():\n",
" i = en_coefs_df.index.get_loc(coef_id)\n",
" color = colors[i]\n",
" \n",
" lopfi_lat, lopfi_lon = row['lopfi_lat'], row['lopfi_lon']\n",
" dipla_lat, dipla_lon = row['dipla_lat'], row['dipla_lon']\n",
" \n",
" if row.lopfi != dfu_lopfi:\n",
" lopfi = folium.CircleMarker(location=[lopfi_lat, lopfi_lon],\n",
" popup=f'LOPFI:{row.lopfi}', color=color, fill=True).add_to(m)\n",
" if row.dipla != dfu_dipla:\n",
" dipla = folium.CircleMarker(location=[dipla_lat, dipla_lon],\n",
" popup=f'LOPFI:{row.dipla}', color=color, fill=True).add_to(m)\n",
" \n",
" coef_line = PolyLine([[lopfi_lat, lopfi_lon],[dipla_lat, dipla_lon]], \n",
" popup=f'id:{coef_id}, coef:{row.coef}',\n",
" weight=row['coef'], color = color).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Coefficients group"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [],
"source": [
"m = Map(location=[30, 0],\n",
" zoom_start=2, tiles=\"CartoDB dark_matter\")\n",
"\n",
"lopfi = CircleMarker(location=[dfu_lopfi_lat, dfu_lopfi_lon],\n",
" popup=f'LOPFI:{dfu_lopfi}', color='red', fill=True).add_to(m)\n",
"dipla = CircleMarker(location=[dfu_dipla_lat, dfu_dipla_lon], \n",
" popup=f'DIPLA:{dfu_dipla}', color='red', fill=True).add_to(m)\n",
"\n",
"enet_group = FeatureGroup(name='ElasticNet coefficients')\n",
"m.add_child(enet_group)\n",
"for coef_id, row in en_coefs_df.iterrows():\n",
" subgroup = FeatureGroupSubGroup(enet_group, coef_id)\n",
" m.add_child(subgroup)\n",
" \n",
" i = en_coefs_df.index.get_loc(coef_id)\n",
" color = colors[i]\n",
" \n",
" lopfi_lat, lopfi_lon = row['lopfi_lat'], row['lopfi_lon']\n",
" dipla_lat, dipla_lon = row['dipla_lat'], row['dipla_lon']\n",
" \n",
" if row.lopfi != dfu_lopfi:\n",
" lopfi = folium.CircleMarker(location=[lopfi_lat, lopfi_lon],\n",
" popup=f'LOPFI:{row.lopfi}', color=color, fill=True).add_to(subgroup)\n",
" if row.dipla != dfu_dipla:\n",
" dipla = folium.CircleMarker(location=[dipla_lat, dipla_lon],\n",
" popup=f'LOPFI:{row.dipla}', color=color, fill=True).add_to(subgroup)\n",
" \n",
" coef_line = PolyLine([[lopfi_lat, lopfi_lon],[dipla_lat, dipla_lon]],\n",
" popup=f'id:{coef_id}, coef:{row.coef}',\n",
" weight=row['coef'], color = color).add_to(subgroup)"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [],
"source": [
"# start with coeff_df: id=DFU, coef=coef\n",
"sgl_h4 = np.load('results/glasso_ev/r95_h4.npy').item()\n",
"sgl_h4 = pd.DataFrame(sgl_h4)\n",
"top = list(sgl_h4.transpose().index)\n",
"sgl_h4_coefs = sgl_h4.transpose()['coef']\n",
"\n",
"sgl_coefs = sgl_h4_coefs[i]\n",
"sgl_coefs = sgl_coefs[:-1]\n",
"sgl_selected = np.nonzero(sgl_coefs)[0]\n",
"sgl_coefs_df = pd.DataFrame(data=sgl_coefs[sgl_selected],index=x.columns[sgl_selected], columns=['coef'])\n",
"sgl_coefs_df['abs_coef'] = np.abs(sgl_coefs_df['coef'])\n",
"sgl_coefs_df = sgl_coefs_df.sort_values(by='abs_coef', ascending=False).head(3)\n",
"\n",
"# From the ID, extract port of origin and port of destination\n",
"sgl_coefs_df['lopfi'] = list(map(lambda s: s.split('_')[0], sgl_coefs_df.index))\n",
"sgl_coefs_df['dipla'] = list(map(lambda s: s.split('_')[1], sgl_coefs_df.index))\n",
"\n",
"sgl_coefs_df = sgl_coefs_df.join(ports, on='lopfi')\n",
"sgl_coefs_df.rename(columns={\"lon\": \"lopfi_lon\", \"lat\": \"lopfi_lat\"}, inplace=True)\n",
"sgl_coefs_df = sgl_coefs_df.join(ports, on='dipla')\n",
"sgl_coefs_df.rename(columns={\"lon\": \"dipla_lon\", \"lat\": \"dipla_lat\"}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f4041308278>"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sgl_group = FeatureGroup(name='GroupLasso coefficients')\n",
"m.add_child(sgl_group)\n",
"for coef_id, row in sgl_coefs_df.iterrows():\n",
" subgroup = FeatureGroupSubGroup(sgl_group, coef_id)\n",
" m.add_child(subgroup)\n",
" \n",
" i = sgl_coefs_df.index.get_loc(coef_id)\n",
" color = colors[i]\n",
" \n",
" lopfi_lat, lopfi_lon = row['lopfi_lat'], row['lopfi_lon']\n",
" dipla_lat, dipla_lon = row['dipla_lat'], row['dipla_lon']\n",
" \n",
" if row.lopfi != dfu_lopfi:\n",
" lopfi = folium.CircleMarker(location=[lopfi_lat, lopfi_lon],\n",
" popup=f'LOPFI:{row.lopfi}', color=color, fill=True).add_to(subgroup)\n",
" if row.dipla != dfu_dipla:\n",
" dipla = folium.CircleMarker(location=[dipla_lat, dipla_lon],\n",
" popup=f'LOPFI:{row.dipla}', color=color, fill=True).add_to(subgroup)\n",
" \n",
" coef_line = PolyLine([[lopfi_lat, lopfi_lon],[dipla_lat, dipla_lon]],\n",
" popup=f'id:{coef_id}, coef:{row.coef}',\n",
" weight=row['coef'], color = color).add_to(subgroup)\n",
" \n",
"LayerControl().add_to(m)\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py36]",
"language": "python",
"name": "conda-env-py36-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
},
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}
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