IMPORTING LIBRARIES¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
!pip install prophet
from prophet import Prophet
Requirement already satisfied: prophet in /usr/local/lib/python3.10/dist-packages (1.1.4) Requirement already satisfied: cmdstanpy>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.2.0) Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.23.5) Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from prophet) (3.7.1) Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.5.3) Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.10/dist-packages (from prophet) (0.0.9) Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.10/dist-packages (from prophet) (2.4.0) Requirement already satisfied: holidays>=0.25 in /usr/local/lib/python3.10/dist-packages (from prophet) (0.34) Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from prophet) (2.8.2) Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from prophet) (4.66.1) Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from prophet) (6.1.0) Requirement already satisfied: stanio~=0.3.0 in /usr/local/lib/python3.10/dist-packages (from cmdstanpy>=1.0.4->prophet) (0.3.0) Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.10/dist-packages (from convertdate>=2.1.2->prophet) (0.5.12) Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.10/dist-packages (from LunarCalendar>=0.0.9->prophet) (4.1.5) Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from LunarCalendar>=0.0.9->prophet) (2023.3.post1) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.1.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (4.43.1) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.4.5) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (23.2) Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (9.4.0) Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (3.1.1) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->prophet) (1.16.0)
IMPORTING DATASETS¶
In [3]:
pc2309 = pd.read_parquet('https://storage.data.gov.my/pricecatcher/pricecatcher_2023-09.parquet')
pc2308 = pd.read_parquet('https://storage.data.gov.my/pricecatcher/pricecatcher_2023-08.parquet')
pc2307 = pd.read_parquet('https://storage.data.gov.my/pricecatcher/pricecatcher_2023-07.parquet')
pc2306 = pd.read_parquet('https://storage.data.gov.my/pricecatcher/pricecatcher_2023-06.parquet')
lookupPremise = pd.read_parquet('https://storage.data.gov.my/pricecatcher/lookup_premise.parquet')
lookupItem = pd.read_parquet('https://storage.data.gov.my/pricecatcher/lookup_item.parquet')
In [4]:
print(pc2309.shape)
print(pc2308.shape)
print(pc2307.shape)
print(pc2306.shape)
(1133814, 4) (2485071, 4) (2678825, 4) (2414728, 4)
MERGING DATASETS¶
In [5]:
priceCatcher = pd.concat([pc2306, pc2307, pc2308, pc2309])
priceCatcher.shape
Out[5]:
(8712438, 4)
In [6]:
PC = priceCatcher.merge(lookupPremise, on='premise_code')
PC = PC.merge(lookupItem, on='item_code')
PC.head()
Out[6]:
| date | premise_code | item_code | price | premise | address | premise_type | state | district | item | unit | item_group | item_category | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-06-01 | 2 | 1 | 9.4 | PASAR BESAR IPOH | JALAN LAKSAMANA,TAMAN JUBILEE,30300 IPOH, PERAK | Pasar Basah | Perak | Kinta | AYAM BERSIH - STANDARD | 1kg | BARANGAN SEGAR | AYAM |
| 1 | 2023-06-02 | 2 | 1 | 9.4 | PASAR BESAR IPOH | JALAN LAKSAMANA,TAMAN JUBILEE,30300 IPOH, PERAK | Pasar Basah | Perak | Kinta | AYAM BERSIH - STANDARD | 1kg | BARANGAN SEGAR | AYAM |
| 2 | 2023-06-03 | 2 | 1 | 9.4 | PASAR BESAR IPOH | JALAN LAKSAMANA,TAMAN JUBILEE,30300 IPOH, PERAK | Pasar Basah | Perak | Kinta | AYAM BERSIH - STANDARD | 1kg | BARANGAN SEGAR | AYAM |
| 3 | 2023-06-04 | 2 | 1 | 9.4 | PASAR BESAR IPOH | JALAN LAKSAMANA,TAMAN JUBILEE,30300 IPOH, PERAK | Pasar Basah | Perak | Kinta | AYAM BERSIH - STANDARD | 1kg | BARANGAN SEGAR | AYAM |
| 4 | 2023-06-06 | 2 | 1 | 9.4 | PASAR BESAR IPOH | JALAN LAKSAMANA,TAMAN JUBILEE,30300 IPOH, PERAK | Pasar Basah | Perak | Kinta | AYAM BERSIH - STANDARD | 1kg | BARANGAN SEGAR | AYAM |
In [7]:
PC['date'] = pd.to_datetime(PC['date'])
PC = PC.sort_values(by='date')
PC.reset_index(drop=True, inplace=True)
PC.head()
Out[7]:
| date | premise_code | item_code | price | premise | address | premise_type | state | district | item | unit | item_group | item_category | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2023-06-01 | 2 | 1 | 9.4 | PASAR BESAR IPOH | JALAN LAKSAMANA,TAMAN JUBILEE,30300 IPOH, PERAK | Pasar Basah | Perak | Kinta | AYAM BERSIH - STANDARD | 1kg | BARANGAN SEGAR | AYAM |
| 1 | 2023-06-01 | 10855 | 360 | 1.3 | MYDIN ( SINAR KOTA ) | BANGUNAN SINAR KOTA,NO. 58, JALAN TUN PERAK, | Pasar Raya / Supermarket | W.P. Kuala Lumpur | Bukit Bintang | SUP BUNJUT ADABI | 8 g | BARANGAN BERBUNGKUS | REMPAH RATUS (BERBUNGKUS) |
| 2 | 2023-06-01 | 10935 | 360 | 1.4 | KWEEK CHIU THUNG | AS 0111824 K15,KUALA SANGLANG,02800 KANGAR, PE... | Kedai Runcit | Perlis | Arau | SUP BUNJUT ADABI | 8 g | BARANGAN BERBUNGKUS | REMPAH RATUS (BERBUNGKUS) |
| 3 | 2023-06-01 | 10975 | 360 | 1.4 | TF VALUE MART BATU GAJAH | PLOT 1, JALAN BHARU 1,BANDAR BARU,BATU GAJAH, | Pasar Raya / Supermarket | Perak | Kinta | SUP BUNJUT ADABI | 8 g | BARANGAN BERBUNGKUS | REMPAH RATUS (BERBUNGKUS) |
| 4 | 2023-06-01 | 11136 | 360 | 1.4 | PASARAYA GRAND UNION BUKIT BERUNTUNG | NO 1 JALAN KEMUNTING,BANDAR BUKIT BERUNTUNG,48... | Pasar Raya / Supermarket | Selangor | Hulu Selangor | SUP BUNJUT ADABI | 8 g | BARANGAN BERBUNGKUS | REMPAH RATUS (BERBUNGKUS) |
HANDLING MISSING VALUES¶
In [8]:
PC.columns = PC.columns.str.strip()
PC = PC.drop(columns=['address', 'premise_code', 'item_code', 'premise', 'item', 'unit'])
PC.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 8712438 entries, 0 to 8712437 Data columns (total 7 columns): # Column Dtype --- ------ ----- 0 date datetime64[ns] 1 price float64 2 premise_type object 3 state object 4 district object 5 item_group object 6 item_category object dtypes: datetime64[ns](1), float64(1), object(5) memory usage: 465.3+ MB
In [9]:
print(PC.isnull().sum())
date 0 price 0 premise_type 5 state 5 district 5 item_group 0 item_category 0 dtype: int64
In [10]:
PC= PC.dropna()
In [11]:
print(PC.isnull().sum())
date 0 price 0 premise_type 0 state 0 district 0 item_group 0 item_category 0 dtype: int64
In [12]:
# PC.to_csv('PriceCatcher_June-Sept23', index=False)
EDA¶
In [13]:
PC['premise_type'].value_counts()
Out[13]:
Pasar Raya / Supermarket 4507392 Pasar Basah 1998052 Hypermarket 1432124 Pasar Mini 463575 Kedai Runcit 309192 Borong 2098 Name: premise_type, dtype: int64
In [14]:
PC['item_group'].value_counts()
Out[14]:
BARANGAN SEGAR 4820470 BARANGAN BERBUNGKUS 2612496 BARANGAN KERING 980593 SUSU DAN BARANGAN BAYI 188957 MINUMAN 60949 PRODUK KEBERSIHAN 48968 Name: item_group, dtype: int64
In [15]:
avgPrice_byState = pd.pivot_table(PC, values='price', index='item_category', columns='state', aggfunc='mean' )
avgPrice_byState['AVG'] = avgPrice_byState.mean(axis=1)
avgPrice_byState = pd.DataFrame(avgPrice_byState)
avgPrice_byState.head()
Out[15]:
| state | Johor | Kedah | Kelantan | Melaka | Negeri Sembilan | Pahang | Perak | Perlis | Pulau Pinang | Sabah | Sarawak | Selangor | Terengganu | W.P. Kuala Lumpur | W.P. Labuan | W.P. Putrajaya | AVG |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| item_category | |||||||||||||||||
| AYAM | 13.577179 | 12.903442 | 13.272509 | 13.089349 | 13.471475 | 13.481781 | 13.593067 | 12.420039 | 13.856764 | 15.464178 | 14.199018 | 14.095669 | 13.603373 | 14.287011 | 15.974913 | 14.259325 | 13.846818 |
| BAHAN LAUT | 24.496317 | 23.591666 | 23.368715 | 25.298830 | 25.179890 | 25.944774 | 24.764527 | 21.014229 | 24.698653 | 16.993632 | 22.824065 | 27.820819 | 23.155818 | 32.740916 | 14.504126 | 30.589502 | 24.186655 |
| BAHAN-BAHAN MINUMAN | 13.237343 | 12.707731 | 14.143296 | 13.008610 | 12.621881 | 13.237875 | 12.742415 | 13.494410 | 12.770362 | 14.074168 | 14.445604 | 12.725152 | 14.467247 | 13.051727 | 14.117201 | 13.253155 | 13.381136 |
| BAWANG | 6.265794 | 6.580701 | 5.789411 | 6.876055 | 6.787348 | 6.027885 | 6.795966 | 5.888576 | 7.259377 | 7.594795 | 7.077658 | 6.578446 | 5.512450 | 7.153185 | 7.791461 | 6.300299 | 6.642463 |
| BERAS | 21.096740 | 18.606463 | 16.339064 | 21.078862 | 22.636934 | 18.660994 | 19.331672 | 14.452741 | 20.990872 | 28.188903 | 29.572137 | 21.295220 | 21.356814 | 21.458685 | 28.432201 | 22.579985 | 21.629893 |
In [16]:
plt.figure(figsize=(12, 8))
sns.heatmap(avgPrice_byState, annot=True, cmap='coolwarm', fmt=".2f", cbar=True)
# Customize the heatmap appearance (optional)
plt.title('Item Prices by State')
plt.xlabel('State')
plt.ylabel('Item Category')
plt.show()
In [17]:
avgPrice_byPremise = pd.pivot_table(PC, values='price', index='item_category', columns='premise_type', aggfunc='mean' )
avgPrice_byPremise['AVG'] = avgPrice_byPremise.mean(axis=1)
avgPrice_byPremise = pd.DataFrame(avgPrice_byPremise)
avgPrice_byPremise.head()
Out[17]:
| premise_type | Borong | Hypermarket | Kedai Runcit | Pasar Basah | Pasar Mini | Pasar Raya / Supermarket | AVG |
|---|---|---|---|---|---|---|---|
| item_category | |||||||
| AYAM | 9.002885 | 13.983422 | 12.996915 | 13.321324 | 13.425203 | 14.120830 | 12.808430 |
| BAHAN LAUT | 23.107947 | 27.144325 | 21.862335 | 24.482651 | 23.019843 | 26.590156 | 24.367876 |
| BAHAN-BAHAN MINUMAN | NaN | 12.859691 | 14.239569 | 14.501149 | 13.368043 | 13.267463 | 13.647183 |
| BAWANG | 6.416969 | 5.818822 | 6.803070 | 7.812760 | 6.832601 | 6.199533 | 6.647293 |
| BERAS | 5.738710 | 24.211281 | 18.893472 | 10.042210 | 20.389553 | 22.922370 | 17.032933 |
In [18]:
plt.figure(figsize=(12, 8))
sns.heatmap(avgPrice_byPremise, annot=True, cmap='coolwarm', fmt=".2f", cbar=True)
# Customize the heatmap appearance (optional)
plt.title('Item Prices by Premise Type')
plt.xlabel('Premise Type')
plt.ylabel('Item Category')
plt.show()
In [19]:
avgPrice = pd.pivot_table(PC, values='price', index='date', columns='item_category', aggfunc='mean')
avgPrice = avgPrice.fillna(method='ffill')
avgPrice = avgPrice.fillna(method='bfill')
avgPrice.head()
Out[19]:
| item_category | AYAM | BAHAN LAUT | BAHAN-BAHAN MINUMAN | BAWANG | BERAS | BIHUN | BUAH-BUAHAN | CILI KERING | DAGING | ESEN DAN RAGI | ... | ROTI | SANTAN (KOTAK) | SAPUAN (SPREADS) | SAYUR-SAYURAN | SUSU BAYI | TAUHU DAN TEMPE | TELUR | TEPUNG | TERSEDIA MINUM | UBI KENTANG |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | |||||||||||||||||||||
| 2023-06-01 | 14.491983 | 26.258987 | 12.07 | 6.309297 | 22.030487 | 3.307986 | 6.418258 | 26.314876 | 31.101939 | 2.85318 | ... | 3.475475 | 6.56961 | 9.294172 | 9.173257 | 41.597994 | 1.028246 | 7.594105 | 3.259209 | 3.370769 | 4.716957 |
| 2023-06-02 | 14.016786 | 26.530345 | 12.07 | 6.252842 | 5.900000 | 3.307986 | 6.305826 | 26.593060 | 31.262115 | 2.85318 | ... | 3.475475 | 6.56961 | 9.294172 | 9.021693 | 41.597994 | 1.026902 | 7.678211 | 3.259209 | 3.370769 | 4.973832 |
| 2023-06-03 | 14.012453 | 26.399959 | 12.07 | 6.281520 | 5.900000 | 3.307986 | 6.439086 | 26.646753 | 31.943195 | 2.85318 | ... | 3.475475 | 6.56961 | 9.294172 | 9.185592 | 41.597994 | 1.049535 | 7.597921 | 3.259209 | 3.370769 | 4.855959 |
| 2023-06-04 | 14.000327 | 26.349349 | 12.07 | 6.258423 | 19.700000 | 3.283333 | 6.445009 | 26.652986 | 31.908968 | 2.85000 | ... | 3.475000 | 6.29750 | 9.561739 | 9.182333 | 40.304348 | 1.053762 | 7.598975 | 3.636667 | 3.370769 | 4.856983 |
| 2023-06-06 | 14.083487 | 26.297032 | 12.07 | 6.333660 | 19.700000 | 3.283333 | 6.473730 | 26.840372 | 31.768142 | 2.85000 | ... | 3.475000 | 6.29750 | 9.561739 | 9.211716 | 40.304348 | 1.042628 | 7.385803 | 3.636667 | 3.370769 | 4.911215 |
5 rows × 38 columns
In [20]:
max_prices = avgPrice.max(axis=0)
min_prices = avgPrice.min(axis=0)
price_summary = pd.DataFrame({'Max Price': max_prices, 'Min Price': min_prices})
print(price_summary)
Max Price Min Price item_category AYAM 14.491983 13.531901 BAHAN LAUT 26.674810 24.575133 BAHAN-BAHAN MINUMAN 18.200000 11.623636 BAWANG 7.145330 6.252842 BERAS 32.250000 5.566667 BIHUN 4.250000 2.290000 BUAH-BUAHAN 6.607857 6.154411 CILI KERING 27.169925 26.189802 DAGING 32.498956 30.966731 ESEN DAN RAGI 4.745000 1.475000 GULA 3.366667 2.512500 HASIL LAUT KERING 27.892373 24.776027 IKAN DALAM TIN 7.986667 4.850000 IKAN DARAT 13.558699 13.083583 KACANG 9.527280 9.174390 KELAPA 7.535247 6.840448 KICAP DAN SOS 5.933333 4.380000 KRIMER DAN SUSU TEPUNG 30.950000 3.933333 LAMPIN PAKAI BUANG 49.950000 28.900000 MAKANAN BAYI 15.500000 7.625000 MEE/KUETIAU 1.736316 1.666648 MENTEGA 14.825000 11.900000 MI SEGERA 5.787500 4.470000 MINYAK DAN LEMAK 22.424273 21.723884 PENJAGAAN DIRI 13.600000 6.828000 PENJAGAAN RUMAH 11.739048 5.623333 REMPAH RATUS (BERBUNGKUS) 6.511111 0.800000 REMPAH RATUS (TIDAK BERBUNGKUS) 5.963636 2.861111 ROTI 4.000000 3.350000 SANTAN (KOTAK) 11.990000 2.900000 SAPUAN (SPREADS) 10.905556 7.250000 SAYUR-SAYURAN 9.493243 8.883599 SUSU BAYI 57.463333 25.367500 TAUHU DAN TEMPE 1.104851 1.026902 TELUR 7.678211 6.830642 TEPUNG 4.110000 2.533333 TERSEDIA MINUM 4.490000 2.000000 UBI KENTANG 5.047877 4.658402
In [21]:
price_summary.plot(kind='bar', figsize=(15, 6))
plt.title('Maximum and Minimum Prices by Item Category')
plt.xlabel('Item Category')
plt.ylabel('Price')
plt.xticks(rotation=90)
plt.legend(loc='upper right')
plt.show()
MODELING¶
In [22]:
items = PC['item_group'].unique()
print(items)
states = PC['state'].unique()
print(states)
['BARANGAN SEGAR' 'BARANGAN BERBUNGKUS' 'BARANGAN KERING' 'SUSU DAN BARANGAN BAYI' 'MINUMAN' 'PRODUK KEBERSIHAN'] ['Perak' 'W.P. Kuala Lumpur' 'Perlis' 'Selangor' 'Melaka' 'Kelantan' 'Negeri Sembilan' 'Terengganu' 'Kedah' 'Pahang' 'Sabah' 'Johor' 'W.P. Putrajaya' 'Pulau Pinang' 'W.P. Labuan' 'Sarawak']
In [25]:
#method to predict item prices by state
import matplotlib.dates as mdates
def forecasting(state):
item_groups = PC['item_group'].unique() # Get unique item groups
num_groups = len(item_groups)
num_rows = 3
num_cols = 2
fig, axs = plt.subplots(num_rows, num_cols, figsize=(15, 15))
fig.subplots_adjust(hspace=0.5) # Add vertical space between subplots
axs = axs.ravel()
for i, item_group in enumerate(item_groups):
item_group_data = PC[(PC['item_group'] == item_group) & (PC['state'] == state)]
item_group_data = item_group_data.groupby(['date'])['price'].mean().reset_index()
item_group_data = pd.DataFrame(item_group_data)
item_group_data = item_group_data.rename(columns={'date': 'ds', 'price': 'y'})
model = Prophet()
model.fit(item_group_data)
future = model.make_future_dataframe(periods=50)
forecast = model.predict(future)
ax = axs[i]
model.plot(forecast, ax=ax)
ax.set_title('Price Forecast for ' + item_group + ' in ' + state)
ax.set_xlabel('Date')
ax.set_ylabel('Price')
# Rotate x-labels
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1)) # Show every month
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
for i in range(num_groups, num_rows * num_cols):
fig.delaxes(axs[i])
plt.tight_layout()
plt.show()
item_groups = PC['item_group'].unique()
In [26]:
forecasting('Kedah')
INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/b2eqslfu.json DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/1nw1nl24.json DEBUG:cmdstanpy:idx 0 DEBUG:cmdstanpy:running CmdStan, num_threads: None DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=3754', 'data', 'file=/tmp/tmpsxix69_c/b2eqslfu.json', 'init=/tmp/tmpsxix69_c/1nw1nl24.json', 'output', 'file=/tmp/tmpsxix69_c/prophet_modelwqrlmha5/prophet_model-20231013161809.csv', 'method=optimize', 'algorithm=newton', 'iter=10000'] 16:18:09 - cmdstanpy - INFO - Chain [1] start processing INFO:cmdstanpy:Chain [1] start processing 16:18:10 - cmdstanpy - INFO - Chain [1] done processing INFO:cmdstanpy:Chain [1] done processing INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/c4ggpsdk.json DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/0c5lhi90.json DEBUG:cmdstanpy:idx 0 DEBUG:cmdstanpy:running CmdStan, num_threads: None DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=77711', 'data', 'file=/tmp/tmpsxix69_c/c4ggpsdk.json', 'init=/tmp/tmpsxix69_c/0c5lhi90.json', 'output', 'file=/tmp/tmpsxix69_c/prophet_modelo6tp_a57/prophet_model-20231013161812.csv', 'method=optimize', 'algorithm=newton', 'iter=10000'] 16:18:12 - cmdstanpy - INFO - Chain [1] start processing INFO:cmdstanpy:Chain [1] start processing 16:18:12 - cmdstanpy - INFO - Chain [1] done processing INFO:cmdstanpy:Chain [1] done processing INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/hcjoydvh.json DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/wg9o8o3l.json DEBUG:cmdstanpy:idx 0 DEBUG:cmdstanpy:running CmdStan, num_threads: None DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=42012', 'data', 'file=/tmp/tmpsxix69_c/hcjoydvh.json', 'init=/tmp/tmpsxix69_c/wg9o8o3l.json', 'output', 'file=/tmp/tmpsxix69_c/prophet_modeln52kbsgr/prophet_model-20231013161814.csv', 'method=optimize', 'algorithm=newton', 'iter=10000'] 16:18:14 - cmdstanpy - INFO - Chain [1] start processing INFO:cmdstanpy:Chain [1] start processing 16:18:14 - cmdstanpy - INFO - Chain [1] done processing INFO:cmdstanpy:Chain [1] done processing INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 11. DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/sy456t7i.json DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/vg8_27b8.json DEBUG:cmdstanpy:idx 0 DEBUG:cmdstanpy:running CmdStan, num_threads: None DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=14761', 'data', 'file=/tmp/tmpsxix69_c/sy456t7i.json', 'init=/tmp/tmpsxix69_c/vg8_27b8.json', 'output', 'file=/tmp/tmpsxix69_c/prophet_modelujlkt0ol/prophet_model-20231013161816.csv', 'method=optimize', 'algorithm=newton', 'iter=10000'] 16:18:16 - cmdstanpy - INFO - Chain [1] start processing INFO:cmdstanpy:Chain [1] start processing 16:18:16 - cmdstanpy - INFO - Chain [1] done processing INFO:cmdstanpy:Chain [1] done processing INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 1. DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/w7ezd_0_.json DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/gfz2_s0j.json DEBUG:cmdstanpy:idx 0 DEBUG:cmdstanpy:running CmdStan, num_threads: None DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=7210', 'data', 'file=/tmp/tmpsxix69_c/w7ezd_0_.json', 'init=/tmp/tmpsxix69_c/gfz2_s0j.json', 'output', 'file=/tmp/tmpsxix69_c/prophet_modelp7d__4tq/prophet_model-20231013161817.csv', 'method=optimize', 'algorithm=newton', 'iter=10000'] 16:18:17 - cmdstanpy - INFO - Chain [1] start processing INFO:cmdstanpy:Chain [1] start processing 16:18:20 - cmdstanpy - INFO - Chain [1] done processing INFO:cmdstanpy:Chain [1] done processing INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this. INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 1. DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/zwnlu0kx.json DEBUG:cmdstanpy:input tempfile: /tmp/tmpsxix69_c/h3tjkx0f.json DEBUG:cmdstanpy:idx 0 DEBUG:cmdstanpy:running CmdStan, num_threads: None DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=88904', 'data', 'file=/tmp/tmpsxix69_c/zwnlu0kx.json', 'init=/tmp/tmpsxix69_c/h3tjkx0f.json', 'output', 'file=/tmp/tmpsxix69_c/prophet_model5a1kef7l/prophet_model-20231013161822.csv', 'method=optimize', 'algorithm=newton', 'iter=10000'] 16:18:22 - cmdstanpy - INFO - Chain [1] start processing INFO:cmdstanpy:Chain [1] start processing 16:18:25 - cmdstanpy - INFO - Chain [1] done processing INFO:cmdstanpy:Chain [1] done processing
