| \n", " | date_week | \n", "y | \n", "x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "dayofyear | \n", "
|---|---|---|---|---|---|---|---|
| 0 | \n", "2018-04-02 | \n", "3.984662 | \n", "0.318580 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "92 | \n", "
| 1 | \n", "2018-04-09 | \n", "3.762872 | \n", "0.112388 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "99 | \n", "
| 2 | \n", "2018-04-16 | \n", "4.466967 | \n", "0.292400 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "106 | \n", "
| 3 | \n", "2018-04-23 | \n", "3.864219 | \n", "0.071399 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "113 | \n", "
| 4 | \n", "2018-04-30 | \n", "4.441625 | \n", "0.386745 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "120 | \n", "
| \n", " | date_week | \n", "y | \n", "x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "dayofyear | \n", "t | \n", "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", "2018-04-02 | \n", "3.984662 | \n", "0.318580 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "92 | \n", "0 | \n", "
| 1 | \n", "2018-04-09 | \n", "3.762872 | \n", "0.112388 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "99 | \n", "1 | \n", "
| 2 | \n", "2018-04-16 | \n", "4.466967 | \n", "0.292400 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "106 | \n", "2 | \n", "
| 3 | \n", "2018-04-23 | \n", "3.864219 | \n", "0.071399 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "113 | \n", "3 | \n", "
| 4 | \n", "2018-04-30 | \n", "4.441625 | \n", "0.386745 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "120 | \n", "4 | \n", "
<xarray.Dataset> Size: 64MB\n",
"Dimensions: (chain: 4, draw: 1000, channel: 2,\n",
" date: 179, control: 3, fourier_mode: 4)\n",
"Coordinates:\n",
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" * date (date) datetime64[ns] 1kB 2018-04-02 ......\n",
" * control (control) <U7 84B 'event_1' 'event_2' 't'\n",
" * fourier_mode (fourier_mode) <U5 80B 'sin_1' ... 'cos_2'\n",
"Data variables: (12/13)\n",
" adstock_alpha (chain, draw, channel) float64 64kB 0.37...\n",
" channel_contributions (chain, draw, date, channel) float64 11MB ...\n",
" control_contributions (chain, draw, date, control) float64 17MB ...\n",
" fourier_contributions (chain, draw, date, fourier_mode) float64 23MB ...\n",
" gamma_control (chain, draw, control) float64 96kB 0.17...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n",
" ... ...\n",
" mu (chain, draw, date) float64 6MB 0.5024 ....\n",
" saturation_beta (chain, draw, channel) float64 64kB 0.33...\n",
" saturation_lam (chain, draw, channel) float64 64kB 3.86...\n",
" total_contributions (chain, draw) float64 32kB 38.0 ... 43.7\n",
" y_sigma (chain, draw) float64 32kB 0.03083 ... 0...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 6MB -0.00084...\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:54.250230+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.16.1\n",
" sampling_time: 13.58799\n",
" tuning_steps: 1000<xarray.Dataset> Size: 204kB\n",
"Dimensions: (chain: 4, draw: 1000)\n",
"Coordinates:\n",
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" * draw (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
"Data variables:\n",
" acceptance_rate (chain, draw) float64 32kB 0.9917 0.8058 ... 0.9826 0.993\n",
" diverging (chain, draw) bool 4kB False False False ... False False\n",
" energy (chain, draw) float64 32kB -339.1 -339.2 ... -339.1 -335.4\n",
" lp (chain, draw) float64 32kB -345.7 -346.1 ... -346.9 -346.1\n",
" n_steps (chain, draw) int64 32kB 511 511 511 511 ... 511 511 511\n",
" step_size (chain, draw) float64 32kB 0.006979 0.006979 ... 0.006447\n",
" tree_depth (chain, draw) int64 32kB 9 9 9 9 9 8 9 10 ... 9 9 9 9 9 9 9\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:54.264810+00:00\n",
" arviz_version: 0.20.0<xarray.Dataset> Size: 32MB\n",
"Dimensions: (chain: 1, draw: 2000, channel: 2,\n",
" date: 179, control: 3, fourier_mode: 4)\n",
"Coordinates:\n",
" * chain (chain) int64 8B 0\n",
" * draw (draw) int64 16kB 0 1 2 ... 1997 1998 1999\n",
" * channel (channel) <U2 16B 'x1' 'x2'\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 ......\n",
" * control (control) <U7 84B 'event_1' 'event_2' 't'\n",
" * fourier_mode (fourier_mode) <U5 80B 'sin_1' ... 'cos_2'\n",
"Data variables: (12/13)\n",
" adstock_alpha (chain, draw, channel) float64 32kB 0.55...\n",
" channel_contributions (chain, draw, date, channel) float64 6MB ...\n",
" control_contributions (chain, draw, date, control) float64 9MB ...\n",
" fourier_contributions (chain, draw, date, fourier_mode) float64 11MB ...\n",
" gamma_control (chain, draw, control) float64 48kB -0.0...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 64kB ...\n",
" ... ...\n",
" mu (chain, draw, date) float64 3MB 1.042 .....\n",
" saturation_beta (chain, draw, channel) float64 32kB 0.12...\n",
" saturation_lam (chain, draw, channel) float64 32kB 3.18...\n",
" total_contributions (chain, draw) float64 16kB 24.58 ... 9.465\n",
" y_sigma (chain, draw) float64 16kB 4.913 ... 4.627\n",
" yearly_seasonality_contribution (chain, draw, date) float64 3MB 0.4942 ....\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:38.508011+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.20.0<xarray.Dataset> Size: 3MB\n",
"Dimensions: (chain: 1, draw: 2000, date: 179)\n",
"Coordinates:\n",
" * chain (chain) int64 8B 0\n",
" * draw (draw) int64 16kB 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
"Data variables:\n",
" y (chain, draw, date) float64 3MB 10.64 1.141 ... 0.5361 -2.597\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:38.511725+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.20.0<xarray.Dataset> Size: 3kB\n",
"Dimensions: (date: 179)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
"Data variables:\n",
" y (date) float64 1kB 0.4794 0.4527 0.5374 ... 0.4978 0.5388 0.5625\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:54.265945+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.16.1\n",
" sampling_time: 13.58799\n",
" tuning_steps: 1000<xarray.Dataset> Size: 9kB\n",
"Dimensions: (date: 179, channel: 2, control: 3)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
" * channel (channel) <U2 16B 'x1' 'x2'\n",
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"Data variables:\n",
" channel_data (date, channel) float64 3kB 0.3196 0.0 0.1128 ... 0.4403 0.0\n",
" control_data (date, control) float64 4kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 178.0\n",
" dayofyear (date) int32 716B 92 99 106 113 120 ... 214 221 228 235 242\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:54.267940+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.16.1\n",
" sampling_time: 13.58799\n",
" tuning_steps: 1000<xarray.Dataset> Size: 12kB\n",
"Dimensions: (index: 179)\n",
"Coordinates:\n",
" * index (index) int64 1kB 0 1 2 3 4 5 6 7 ... 172 173 174 175 176 177 178\n",
"Data variables:\n",
" date_week (index) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
" x1 (index) float64 1kB 0.3186 0.1124 0.2924 ... 0.1719 0.2803 0.4389\n",
" x2 (index) float64 1kB 0.0 0.0 0.0 0.0 0.0 ... 0.8633 0.0 0.0 0.0\n",
" event_1 (index) float64 1kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
" event_2 (index) float64 1kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
" dayofyear (index) int32 716B 92 99 106 113 120 127 ... 214 221 228 235 242\n",
" t (index) int64 1kB 0 1 2 3 4 5 6 7 ... 172 173 174 175 176 177 178\n",
" y (index) float64 1kB 3.985 3.763 4.467 3.864 ... 4.138 4.479 4.676<xarray.Dataset> Size: 64MB\n",
"Dimensions: (chain: 4, draw: 1000, channel: 2,\n",
" date: 179, control: 3, fourier_mode: 4)\n",
"Coordinates:\n",
" * chain (chain) int64 32B 0 1 2 3\n",
" * draw (draw) int64 8kB 0 1 2 3 ... 997 998 999\n",
" * channel (channel) <U2 16B 'x1' 'x2'\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 ......\n",
" * control (control) <U7 84B 'event_1' 'event_2' 't'\n",
" * fourier_mode (fourier_mode) <U5 80B 'sin_1' ... 'cos_2'\n",
"Data variables: (12/13)\n",
" adstock_alpha (chain, draw, channel) float64 64kB 0.37...\n",
" channel_contributions (chain, draw, date, channel) float64 11MB ...\n",
" control_contributions (chain, draw, date, control) float64 17MB ...\n",
" fourier_contributions (chain, draw, date, fourier_mode) float64 23MB ...\n",
" gamma_control (chain, draw, control) float64 96kB 0.17...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n",
" ... ...\n",
" mu (chain, draw, date) float64 6MB 0.5024 ....\n",
" saturation_beta (chain, draw, channel) float64 64kB 0.33...\n",
" saturation_lam (chain, draw, channel) float64 64kB 3.86...\n",
" total_contributions (chain, draw) float64 32kB 38.0 ... 43.7\n",
" y_sigma (chain, draw) float64 32kB 0.03083 ... 0...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 6MB -0.00084...\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:54.250230+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.16.1\n",
" sampling_time: 13.58799\n",
" tuning_steps: 1000| \n", " | mean | \n", "sd | \n", "hdi_3% | \n", "hdi_97% | \n", "mcse_mean | \n", "mcse_sd | \n", "ess_bulk | \n", "ess_tail | \n", "r_hat | \n", "
|---|---|---|---|---|---|---|---|---|---|
| intercept | \n", "0.355 | \n", "0.013 | \n", "0.330 | \n", "0.380 | \n", "0.000 | \n", "0.000 | \n", "2585.0 | \n", "2711.0 | \n", "1.0 | \n", "
| y_sigma | \n", "0.031 | \n", "0.002 | \n", "0.028 | \n", "0.035 | \n", "0.000 | \n", "0.000 | \n", "3182.0 | \n", "2862.0 | \n", "1.0 | \n", "
| saturation_beta[x1] | \n", "0.362 | \n", "0.020 | \n", "0.326 | \n", "0.402 | \n", "0.000 | \n", "0.000 | \n", "2218.0 | \n", "2375.0 | \n", "1.0 | \n", "
| saturation_beta[x2] | \n", "0.270 | \n", "0.083 | \n", "0.193 | \n", "0.396 | \n", "0.003 | \n", "0.002 | \n", "1404.0 | \n", "1101.0 | \n", "1.0 | \n", "
| saturation_lam[x1] | \n", "3.952 | \n", "0.379 | \n", "3.232 | \n", "4.637 | \n", "0.007 | \n", "0.005 | \n", "2566.0 | \n", "2270.0 | \n", "1.0 | \n", "
| saturation_lam[x2] | \n", "3.140 | \n", "1.188 | \n", "1.074 | \n", "5.356 | \n", "0.031 | \n", "0.022 | \n", "1378.0 | \n", "1134.0 | \n", "1.0 | \n", "
| adstock_alpha[x1] | \n", "0.402 | \n", "0.031 | \n", "0.341 | \n", "0.458 | \n", "0.001 | \n", "0.000 | \n", "2582.0 | \n", "2532.0 | \n", "1.0 | \n", "
| adstock_alpha[x2] | \n", "0.188 | \n", "0.041 | \n", "0.117 | \n", "0.271 | \n", "0.001 | \n", "0.001 | \n", "1820.0 | \n", "1833.0 | \n", "1.0 | \n", "
| gamma_control[event_1] | \n", "0.176 | \n", "0.028 | \n", "0.123 | \n", "0.226 | \n", "0.000 | \n", "0.000 | \n", "3408.0 | \n", "2689.0 | \n", "1.0 | \n", "
| gamma_control[event_2] | \n", "0.231 | \n", "0.028 | \n", "0.178 | \n", "0.282 | \n", "0.000 | \n", "0.000 | \n", "3310.0 | \n", "2774.0 | \n", "1.0 | \n", "
| gamma_control[t] | \n", "0.001 | \n", "0.000 | \n", "0.001 | \n", "0.001 | \n", "0.000 | \n", "0.000 | \n", "3057.0 | \n", "3034.0 | \n", "1.0 | \n", "
| gamma_fourier[sin_1] | \n", "0.003 | \n", "0.003 | \n", "-0.004 | \n", "0.010 | \n", "0.000 | \n", "0.000 | \n", "5758.0 | \n", "2452.0 | \n", "1.0 | \n", "
| gamma_fourier[sin_2] | \n", "-0.058 | \n", "0.004 | \n", "-0.064 | \n", "-0.051 | \n", "0.000 | \n", "0.000 | \n", "5624.0 | \n", "2990.0 | \n", "1.0 | \n", "
| gamma_fourier[cos_1] | \n", "0.062 | \n", "0.003 | \n", "0.057 | \n", "0.069 | \n", "0.000 | \n", "0.000 | \n", "5881.0 | \n", "2848.0 | \n", "1.0 | \n", "
| gamma_fourier[cos_2] | \n", "0.001 | \n", "0.003 | \n", "-0.005 | \n", "0.008 | \n", "0.000 | \n", "0.000 | \n", "5009.0 | \n", "3071.0 | \n", "1.0 | \n", "
<xarray.Dataset> Size: 6MB\n",
"Dimensions: (sample: 4000, date: 179)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
" * sample (sample) object 32kB MultiIndex\n",
" * chain (sample) int64 32kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3\n",
" * draw (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
"Data variables:\n",
" y (date, sample) float64 6MB 3.879 4.181 3.995 ... 4.974 5.116 4.946\n",
"Attributes:\n",
" created_at: 2025-01-25T21:40:56.469125+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.20.0Pipeline(steps=[('scaler', MaxAbsScaler())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scaler', MaxAbsScaler())])MaxAbsScaler()
| \n", " | x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "t | \n", "yearly_seasonality | \n", "intercept | \n", "
|---|---|---|---|---|---|---|---|
| date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
| 2018-04-02 | \n", "1.081818 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.000000 | \n", "0.021384 | \n", "2.948147 | \n", "
| 2018-04-09 | \n", "0.831961 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.005136 | \n", "0.073417 | \n", "2.948147 | \n", "
| 2018-04-16 | \n", "1.292488 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.010273 | \n", "0.119255 | \n", "2.948147 | \n", "
| 2018-04-23 | \n", "0.790950 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.015409 | \n", "0.153584 | \n", "2.948147 | \n", "
| 2018-04-30 | \n", "1.538755 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.020545 | \n", "0.171823 | \n", "2.948147 | \n", "
| \n", " | date_week | \n", "x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "t | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "2021-09-06 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "179 | \n", "
| 1 | \n", "2021-09-13 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "180 | \n", "
| 2 | \n", "2021-09-20 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "181 | \n", "
| 3 | \n", "2021-09-27 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "182 | \n", "
| 4 | \n", "2021-10-04 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "183 | \n", "
<xarray.Dataset> Size: 256kB\n",
"Dimensions: (sample: 4000, date: 5)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 40B 2021-09-06 2021-09-13 ... 2021-10-04\n",
" * sample (sample) object 32kB MultiIndex\n",
" * chain (sample) int64 32kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3\n",
" * draw (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
"Data variables:\n",
" y (date, sample) float64 160kB 5.145 5.333 4.512 ... 5.915 6.603\n",
"Attributes:\n",
" created_at: 2025-01-25T21:41:16.704856+00:00\n",
" arviz_version: 0.20.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.20.0