/
predict_future_sales.ipynb
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predict_future_sales.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predict Future Sales"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/anaconda3/envs/fastai/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"/home/ubuntu/anaconda3/envs/fastai/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"/home/ubuntu/anaconda3/envs/fastai/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
" from numpy.core.umath_tests import inner1d\n"
]
}
],
"source": [
"import sys\n",
"sys.path.append('/home/ubuntu/fastai')\n",
"\n",
"from fastai.structured import *\n",
"from fastai.column_data import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Settings\n",
"\n",
"PATH='data/predict_future_sales/'\n",
"DO_SAMPLE = False"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_pickle(PATH+'processed_data.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date_block_num</th>\n",
" <th>shop_id</th>\n",
" <th>item_id</th>\n",
" <th>item_cnt_month</th>\n",
" <th>city_code</th>\n",
" <th>item_category_id</th>\n",
" <th>type_code</th>\n",
" <th>subtype_code</th>\n",
" <th>item_cnt_month_lag_1</th>\n",
" <th>item_cnt_month_lag_2</th>\n",
" <th>...</th>\n",
" <th>date_subtype_avg_item_cnt_lag_1</th>\n",
" <th>delta_price_lag</th>\n",
" <th>delta_revenue_lag_1</th>\n",
" <th>month</th>\n",
" <th>year</th>\n",
" <th>days</th>\n",
" <th>item_shop_last_sale</th>\n",
" <th>item_last_sale</th>\n",
" <th>item_shop_first_sale</th>\n",
" <th>item_first_sale</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <td>16</td>\n",
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" <td>NaN</td>\n",
" <td>...</td>\n",
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" <td>28.0</td>\n",
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" </tr>\n",
" <tr>\n",
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"</table>\n",
"<p>5 rows × 41 columns</p>\n",
"</div>"
],
"text/plain": [
" date_block_num shop_id item_id item_cnt_month city_code \\\n",
"0 0 2 19 0.0 0 \n",
"1 0 2 27 1.0 0 \n",
"2 0 2 28 0.0 0 \n",
"3 0 2 29 0.0 0 \n",
"4 0 2 32 0.0 0 \n",
"\n",
" item_category_id type_code subtype_code item_cnt_month_lag_1 \\\n",
"0 40 11 4 NaN \n",
"1 19 5 10 NaN \n",
"2 30 8 55 NaN \n",
"3 23 5 16 NaN \n",
"4 40 11 4 NaN \n",
"\n",
" item_cnt_month_lag_2 ... date_subtype_avg_item_cnt_lag_1 \\\n",
"0 NaN ... NaN \n",
"1 NaN ... NaN \n",
"2 NaN ... NaN \n",
"3 NaN ... NaN \n",
"4 NaN ... NaN \n",
"\n",
" delta_price_lag delta_revenue_lag_1 month year days \\\n",
"0 NaN NaN 1 2013 28.0 \n",
"1 NaN NaN 1 2013 28.0 \n",
"2 NaN NaN 1 2013 28.0 \n",
"3 NaN NaN 1 2013 28.0 \n",
"4 NaN NaN 1 2013 28.0 \n",
"\n",
" item_shop_last_sale item_last_sale item_shop_first_sale item_first_sale \n",
"0 -1 -1 0 0 \n",
"1 -1 -1 0 0 \n",
"2 -1 -1 0 0 \n",
"3 -1 -1 0 0 \n",
"4 -1 -1 0 0 \n",
"\n",
"[5 rows x 41 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 11128004 entries, 0 to 11128003\n",
"Data columns (total 41 columns):\n",
"date_block_num int8\n",
"shop_id int8\n",
"item_id int16\n",
"item_cnt_month float16\n",
"city_code int8\n",
"item_category_id int8\n",
"type_code int8\n",
"subtype_code int8\n",
"item_cnt_month_lag_1 float16\n",
"item_cnt_month_lag_2 float16\n",
"item_cnt_month_lag_3 float16\n",
"item_cnt_month_lag_6 float16\n",
"item_cnt_month_lag_12 float16\n",
"date_avg_item_cnt_lag_1 float16\n",
"date_item_avg_item_cnt_lag_1 float16\n",
"date_item_avg_item_cnt_lag_2 float16\n",
"date_item_avg_item_cnt_lag_3 float16\n",
"date_item_avg_item_cnt_lag_6 float16\n",
"date_item_avg_item_cnt_lag_12 float16\n",
"date_shop_avg_item_cnt_lag_1 float16\n",
"date_shop_avg_item_cnt_lag_2 float16\n",
"date_shop_avg_item_cnt_lag_3 float16\n",
"date_shop_avg_item_cnt_lag_6 float16\n",
"date_shop_avg_item_cnt_lag_12 float16\n",
"date_cat_avg_item_cnt_lag_1 float16\n",
"date_shop_cat_avg_item_cnt_lag_1 float16\n",
"date_shop_type_avg_item_cnt_lag_1 float16\n",
"date_shop_subtype_avg_item_cnt_lag_1 float16\n",
"date_city_avg_item_cnt_lag_1 float16\n",
"date_item_city_avg_item_cnt_lag_1 float16\n",
"date_type_avg_item_cnt_lag_1 float16\n",
"date_subtype_avg_item_cnt_lag_1 float16\n",
"delta_price_lag float16\n",
"delta_revenue_lag_1 float16\n",
"month int8\n",
"year int16\n",
"days float64\n",
"item_shop_last_sale int8\n",
"item_last_sale int8\n",
"item_shop_first_sale int8\n",
"item_first_sale int8\n",
"dtypes: float16(27), float64(1), int16(2), int8(11)\n",
"memory usage: 902.1 MB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"dep = 'item_cnt_month'\n",
"\n",
"cat_vars = ['shop_id', 'item_id', 'city_code', 'item_category_id', 'type_code', \n",
" 'subtype_code', 'month', 'year']\n",
"\n",
"contin_vars = ['date_block_num', 'item_cnt_month_lag_1', 'item_cnt_month_lag_2', \n",
" 'item_cnt_month_lag_3', 'item_cnt_month_lag_6', 'item_cnt_month_lag_12', \n",
" 'date_item_avg_item_cnt_lag_1', 'date_item_avg_item_cnt_lag_2', \n",
" 'date_item_avg_item_cnt_lag_3', 'date_item_avg_item_cnt_lag_6',\n",
" 'date_item_avg_item_cnt_lag_12', 'date_shop_avg_item_cnt_lag_1',\n",
" 'date_shop_avg_item_cnt_lag_2', 'date_shop_avg_item_cnt_lag_3',\n",
" 'date_shop_avg_item_cnt_lag_6', 'date_shop_avg_item_cnt_lag_12',\n",
" 'date_cat_avg_item_cnt_lag_1', 'date_shop_cat_avg_item_cnt_lag_1',\n",
" 'date_shop_type_avg_item_cnt_lag_1', 'date_shop_subtype_avg_item_cnt_lag_1',\n",
" 'date_city_avg_item_cnt_lag_1', 'date_item_city_avg_item_cnt_lag_1',\n",
" 'date_type_avg_item_cnt_lag_1', 'date_subtype_avg_item_cnt_lag_1',\n",
" 'delta_price_lag', 'delta_revenue_lag_1',\n",
" 'days', \n",
" 'item_shop_last_sale', 'item_last_sale', \n",
" 'item_shop_first_sale', 'item_first_sale']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(11128004, 41)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10913804, 214200)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = data[data['date_block_num'] != 34]\n",
"test = data[data['date_block_num'] == 34]\n",
"\n",
"len(train), len(test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train = train[cat_vars+contin_vars+[dep]].copy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"test = test[cat_vars+contin_vars+[dep]].copy()\n",
"test[dep] = 0"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"del data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"for v in cat_vars: train[v] = train[v].astype('category').cat.as_ordered()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"apply_cats(test, train)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Pytorch expects float32 numbers\n",
"for v in contin_vars:\n",
" train[v] = train[v].fillna(0).astype('float32')\n",
" test[v] = test[v].fillna(0).astype('float32')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10913804"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n = len(train)\n",
"if DO_SAMPLE:\n",
" dt_size = 200000\n",
" print('Fraction used: '+str(dt_size/n))\n",
" idxs = get_cv_idxs(n, val_pct=dt_size/n)\n",
" samp = train.iloc[idxs]\n",
"else:\n",
" samp = train\n",
"samp_size = len(samp); samp_size"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df, y, nas, mapper = proc_df(samp, dep, do_scale=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"df_test, _, nas, mapper = proc_df(test, dep, do_scale=True, mapper=mapper, na_dict=nas)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Select previous month transactions as validation set\n",
"val_idxs = np.array(range(len(df)))[samp['date_block_num'] == 33]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DL Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: having y_range as (0, 24) is a mistake; should change to (0, 20)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 20.0, (0, 24.0))"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_range = (0, np.max(y) * 1.2) # Just in case\n",
"np.min(y), np.max(y), y_range # As entries are clipped at 20"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"md = ColumnarModelData.from_data_frame(PATH, val_idxs, df, y.astype(np.float32),\n",
" cat_vars, bs = 256, test_df = df_test)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('shop_id', 58),\n",
" ('item_id', 21807),\n",
" ('city_code', 32),\n",
" ('item_category_id', 85),\n",
" ('type_code', 21),\n",
" ('subtype_code', 66),\n",
" ('month', 13),\n",
" ('year', 4)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat_sz = [(c, len(samp[c].cat.categories)+1) for c in cat_vars]\n",
"cat_sz"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(58, 29),\n",
" (21807, 50),\n",
" (32, 16),\n",
" (85, 43),\n",
" (21, 11),\n",
" (66, 33),\n",
" (13, 7),\n",
" (4, 3)]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"emb_szs = [(c, min(50, (c+1)//2)) for _,c in cat_sz]\n",
"emb_szs[len(emb_szs) - 1] = (4, 3) # I'll add an extra embedding for the year manually\n",
"emb_szs"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"m = md.get_learner(emb_szs, len(df.columns) - len(cat_vars), emb_drop=0.04, \n",
" out_sz = 1, szs = [1000, 500], drops = [0.002,0.02], y_range = y_range)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MixedInputModel(\n",
" (embs): ModuleList(\n",
" (0): Embedding(58, 29)\n",
" (1): Embedding(21807, 50)\n",
" (2): Embedding(32, 16)\n",
" (3): Embedding(85, 43)\n",
" (4): Embedding(21, 11)\n",
" (5): Embedding(66, 33)\n",
" (6): Embedding(13, 7)\n",
" (7): Embedding(4, 3)\n",
" )\n",
" (lins): ModuleList(\n",
" (0): Linear(in_features=223, out_features=1000, bias=True)\n",
" (1): Linear(in_features=1000, out_features=500, bias=True)\n",
" )\n",
" (bns): ModuleList(\n",
" (0): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=True)\n",
" (1): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=True)\n",
" )\n",
" (outp): Linear(in_features=500, out_features=1, bias=True)\n",
" (emb_drop): Dropout(p=0.04)\n",
" (drops): ModuleList(\n",
" (0): Dropout(p=0.002)\n",
" (1): Dropout(p=0.02)\n",
" )\n",
" (bn): BatchNorm1d(31, eps=1e-05, momentum=0.1, affine=True)\n",
")"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3058d851463845daa9d421eccb191ff2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss \n",
" 0 1.569774 1.357704 \n",
"\n"
]
}
],
"source": [
"m.lr_find()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m.sched.plot(700)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"lr = 2e-4"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def mse(y_pred, targ):\n",
" err = targ - y_pred\n",
" return math.sqrt((err**2).mean())"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "61bd74c1cc854f8a9d98e8f4b67b6505",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss mse \n",
" 0 0.48113 1.107824 0.906849 \n",
"\n"
]
},
{
"data": {
"text/plain": [
"[array([1.10782]), 0.9068488826528686]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.fit(lr, 1, metrics=[mse])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"m.save('m1')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<a href='data/predict_future_sales/Submission1b.csv' target='_blank'>data/predict_future_sales/Submission1b.csv</a><br>"
],
"text/plain": [
"/home/ubuntu/fastai_projects1/data/predict_future_sales/Submission1b.csv"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fn = PATH+'Submission1b.csv'\n",
"\n",
"preds = m.predict(is_test=True)\n",
"test['item_cnt_month'] = preds\n",
"test['Id'] = list(range(len(test)))\n",
"test[['Id','item_cnt_month']].to_csv(fn, index=False, mode = 'a')\n",
"FileLink(fn)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m.sched.plot_loss()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add learning rate annealing"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "559c24aa128d4942a14579204c2cd9a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=7), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss mse \n",
" 0 0.443312 1.249474 0.943925 \n",
" 1 0.465232 1.387181 0.992528 \n",
" 21%|██ | 8579/41702 [01:18<05:04, 108.72it/s, loss=0.442]"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-34-91b6b86867e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwarm_up\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, best_save_name, use_clr, use_clr_beta, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, use_swa, swa_start, swa_eval_freq, **kwargs)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreg_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp16\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0mswa_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswa_model\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_swa\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswa_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswa_start\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m swa_eval_freq=swa_eval_freq, **kwargs)\n\u001b[0m\u001b[1;32m 252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 253\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/fastai/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, n_epochs, opt, crit, metrics, callbacks, stepper, swa_model, swa_start, swa_eval_freq, visualize, **kwargs)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mall_val\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mval_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mIterBatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 139\u001b[0m \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 929\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m 930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 932\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 933\u001b[0m \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/fastai/fastai/dataloader.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mThreadPoolExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;31m# avoid py3.6 issue where queue is infinite and can result in memory exhaustion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunk_iter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_sampler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 88\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mget_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhalf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/fastai/fastai/core.py\u001b[0m in \u001b[0;36mchunk_iter\u001b[0;34m(iterable, chunk_size)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"m.fit(lr, 3, cycle_len = 1, cycle_mult = 2, metrics=[mse])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Looks like we're over-fitting. Let's change dropout."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"m = md.get_learner(emb_szs, len(df.columns) - len(cat_vars), emb_drop=0.04, \n",
" out_sz = 1, szs = [1000, 500], drops = [0.015,0.1], y_range = y_range)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cb2894dd60d54e4f81220d48b06c69d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss mse \n",
" 0 0.513504 0.935313 0.817439 \n"
]
},
{
"data": {
"text/plain": [
"[array([0.93531]), 0.817439111748875]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.fit(lr, 1, metrics=[mse])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"m.save('m1')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},