| \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, control: 3,\n",
" fourier_mode: 4, channel: 2, date: 179)\n",
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" gamma_control (chain, draw, control) float64 96kB 0.14...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n",
" adstock_alpha (chain, draw, channel) float64 64kB 0.34...\n",
" saturation_lam (chain, draw, channel) float64 64kB 3.89...\n",
" saturation_beta (chain, draw, channel) float64 64kB 0.32...\n",
" ... ...\n",
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" fourier_contribution (chain, draw, date, fourier_mode) float64 23MB ...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 6MB 0.003624...\n",
" mu (chain, draw, date) float64 6MB 0.501 .....\n",
"Attributes:\n",
" created_at: 2025-06-16T17:24:44.055245+00:00\n",
" arviz_version: 0.21.0\n",
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" tuning_steps: 1000\n",
" pymc_marketing_version: 0.14.0<xarray.Dataset> Size: 204kB\n",
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"Attributes:\n",
" created_at: 2025-06-16T17:24:44.072769+00:00\n",
" arviz_version: 0.21.0<xarray.Dataset> Size: 32MB\n",
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"Data variables: (12/13)\n",
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" adstock_alpha (chain, draw, channel) float64 32kB 0.03...\n",
" gamma_control (chain, draw, control) float64 48kB -0.0...\n",
" channel_contribution (chain, draw, date, channel) float64 6MB ...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 3MB 0.1752 ....\n",
" saturation_lam (chain, draw, channel) float64 32kB 2.81...\n",
" ... ...\n",
" control_contribution (chain, draw, date, control) float64 9MB ...\n",
" mu (chain, draw, date) float64 3MB 0.9896 ....\n",
" y_sigma (chain, draw) float64 16kB 2.914 ... 0.5029\n",
" gamma_fourier (chain, draw, fourier_mode) float64 64kB ...\n",
" saturation_beta (chain, draw, channel) float64 32kB 0.17...\n",
" fourier_contribution (chain, draw, date, fourier_mode) float64 11MB ...\n",
"Attributes:\n",
" created_at: 2025-06-16T17:24:26.965365+00:00\n",
" arviz_version: 0.21.0\n",
" inference_library: pymc\n",
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" * 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",
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"Attributes:\n",
" created_at: 2025-06-16T17:24:26.969062+00:00\n",
" arviz_version: 0.21.0\n",
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"Data variables:\n",
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"Attributes:\n",
" created_at: 2025-06-16T17:24:44.074134+00:00\n",
" arviz_version: 0.21.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.18.0\n",
" sampling_time: 15.382141\n",
" tuning_steps: 1000<xarray.Dataset> Size: 9kB\n",
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"Data variables:\n",
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" dayofyear (date) int32 716B 92 99 106 113 120 ... 214 221 228 235 242\n",
"Attributes:\n",
" created_at: 2025-06-16T17:24:44.076514+00:00\n",
" arviz_version: 0.21.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.18.0\n",
" sampling_time: 15.382141\n",
" tuning_steps: 1000<xarray.Dataset> Size: 12kB\n",
"Dimensions: (index: 179)\n",
"Coordinates:\n",
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"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, control: 3,\n",
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" * draw (draw) int64 8kB 0 1 2 3 ... 997 998 999\n",
" * control (control) <U7 84B 'event_1' 'event_2' 't'\n",
" * fourier_mode (fourier_mode) <U5 80B 'sin_1' ... 'cos_2'\n",
" * channel (channel) <U2 16B 'x1' 'x2'\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 ......\n",
"Data variables: (12/13)\n",
" intercept (chain, draw) float64 32kB 0.3718 ... 0....\n",
" gamma_control (chain, draw, control) float64 96kB 0.14...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n",
" adstock_alpha (chain, draw, channel) float64 64kB 0.34...\n",
" saturation_lam (chain, draw, channel) float64 64kB 3.89...\n",
" saturation_beta (chain, draw, channel) float64 64kB 0.32...\n",
" ... ...\n",
" channel_contribution (chain, draw, date, channel) float64 11MB ...\n",
" total_contribution (chain, draw) float64 32kB 37.96 ... 41.48\n",
" control_contribution (chain, draw, date, control) float64 17MB ...\n",
" fourier_contribution (chain, draw, date, fourier_mode) float64 23MB ...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 6MB 0.003624...\n",
" mu (chain, draw, date) float64 6MB 0.501 .....\n",
"Attributes:\n",
" created_at: 2025-06-16T17:24:44.055245+00:00\n",
" arviz_version: 0.21.0\n",
" inference_library: numpyro\n",
" inference_library_version: 0.18.0\n",
" sampling_time: 15.382141\n",
" tuning_steps: 1000\n",
" pymc_marketing_version: 0.14.0| \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", "0.328 | \n", "0.379 | \n", "0.000 | \n", "0.000 | \n", "2468.0 | \n", "2444.0 | \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", "3154.0 | \n", "2659.0 | \n", "1.0 | \n", "
| saturation_beta[x1] | \n", "0.363 | \n", "0.020 | \n", "0.324 | \n", "0.400 | \n", "0.000 | \n", "0.000 | \n", "2006.0 | \n", "2198.0 | \n", "1.0 | \n", "
| saturation_beta[x2] | \n", "0.270 | \n", "0.093 | \n", "0.191 | \n", "0.401 | \n", "0.003 | \n", "0.012 | \n", "1080.0 | \n", "712.0 | \n", "1.0 | \n", "
| saturation_lam[x1] | \n", "3.942 | \n", "0.385 | \n", "3.227 | \n", "4.675 | \n", "0.008 | \n", "0.006 | \n", "2235.0 | \n", "1970.0 | \n", "1.0 | \n", "
| saturation_lam[x2] | \n", "3.162 | \n", "1.210 | \n", "0.899 | \n", "5.309 | \n", "0.035 | \n", "0.035 | \n", "1095.0 | \n", "753.0 | \n", "1.0 | \n", "
| adstock_alpha[x1] | \n", "0.402 | \n", "0.030 | \n", "0.342 | \n", "0.458 | \n", "0.001 | \n", "0.000 | \n", "2451.0 | \n", "2767.0 | \n", "1.0 | \n", "
| adstock_alpha[x2] | \n", "0.187 | \n", "0.041 | \n", "0.108 | \n", "0.264 | \n", "0.001 | \n", "0.001 | \n", "1626.0 | \n", "2127.0 | \n", "1.0 | \n", "
| gamma_control[event_1] | \n", "0.176 | \n", "0.028 | \n", "0.123 | \n", "0.228 | \n", "0.000 | \n", "0.000 | \n", "3924.0 | \n", "2816.0 | \n", "1.0 | \n", "
| gamma_control[event_2] | \n", "0.231 | \n", "0.028 | \n", "0.179 | \n", "0.285 | \n", "0.000 | \n", "0.000 | \n", "3324.0 | \n", "2771.0 | \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", "3317.0 | \n", "3483.0 | \n", "1.0 | \n", "
| gamma_fourier[sin_1] | \n", "0.003 | \n", "0.003 | \n", "-0.004 | \n", "0.009 | \n", "0.000 | \n", "0.000 | \n", "5527.0 | \n", "2956.0 | \n", "1.0 | \n", "
| gamma_fourier[sin_2] | \n", "-0.058 | \n", "0.003 | \n", "-0.064 | \n", "-0.051 | \n", "0.000 | \n", "0.000 | \n", "5269.0 | \n", "2979.0 | \n", "1.0 | \n", "
| gamma_fourier[cos_1] | \n", "0.062 | \n", "0.003 | \n", "0.056 | \n", "0.069 | \n", "0.000 | \n", "0.000 | \n", "6226.0 | \n", "2724.0 | \n", "1.0 | \n", "
| gamma_fourier[cos_2] | \n", "0.001 | \n", "0.004 | \n", "-0.006 | \n", "0.007 | \n", "0.000 | \n", "0.000 | \n", "5045.0 | \n", "2954.0 | \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 3.935 3.864 4.343 ... 5.468 4.626 5.179\n",
"Attributes:\n",
" created_at: 2025-06-16T17:24:54.767542+00:00\n",
" arviz_version: 0.21.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.23.0Pipeline(steps=[('scaler', MaxAbsScaler())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. | \n", " | steps | \n", "[('scaler', ...)] | \n", "
| \n", " | transform_input | \n", "None | \n", "
| \n", " | memory | \n", "None | \n", "
| \n", " | verbose | \n", "False | \n", "
| \n", " | copy | \n", "True | \n", "
| \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", "1.080461 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.000000 | \n", "0.021557 | \n", "2.947933 | \n", "
| 2018-04-09 | \n", "0.831271 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.005135 | \n", "0.073669 | \n", "2.947933 | \n", "
| 2018-04-16 | \n", "1.291448 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.010269 | \n", "0.119551 | \n", "2.947933 | \n", "
| 2018-04-23 | \n", "0.790594 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.015404 | \n", "0.153887 | \n", "2.947933 | \n", "
| 2018-04-30 | \n", "1.537717 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.020539 | \n", "0.172094 | \n", "2.947933 | \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 4.258 4.567 4.333 ... 5.769 5.715\n",
"Attributes:\n",
" created_at: 2025-06-16T17:26:02.195222+00:00\n",
" arviz_version: 0.21.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.23.0