\n", " | date | \n", "year | \n", "month | \n", "dayofyear | \n", "t | \n", "influencer_spend | \n", "shipping_threshold | \n", "intercept | \n", "trend | \n", "cs | \n", "cc | \n", "seasonality | \n", "epsilon | \n", "y | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "2019-04-01 | \n", "2019 | \n", "4 | \n", "91 | \n", "0 | \n", "0.918883 | \n", "25.0 | \n", "2.0 | \n", "0.778279 | \n", "-0.012893 | \n", "0.006446 | \n", "-0.003223 | \n", "-0.118826 | \n", "2.561363 | \n", "
1 | \n", "2019-04-08 | \n", "2019 | \n", "4 | \n", "98 | \n", "1 | \n", "0.230898 | \n", "25.0 | \n", "2.0 | \n", "0.795664 | \n", "0.225812 | \n", "-0.113642 | \n", "0.056085 | \n", "0.064977 | \n", "2.264874 | \n", "
2 | \n", "2019-04-15 | \n", "2019 | \n", "4 | \n", "105 | \n", "2 | \n", "0.254486 | \n", "25.0 | \n", "2.0 | \n", "0.812559 | \n", "0.451500 | \n", "-0.232087 | \n", "0.109706 | \n", "-0.020269 | \n", "1.998208 | \n", "
3 | \n", "2019-04-22 | \n", "2019 | \n", "4 | \n", "112 | \n", "3 | \n", "0.035995 | \n", "25.0 | \n", "2.0 | \n", "0.828993 | \n", "0.651162 | \n", "-0.347175 | \n", "0.151993 | \n", "0.400209 | \n", "1.701116 | \n", "
4 | \n", "2019-04-29 | \n", "2019 | \n", "4 | \n", "119 | \n", "4 | \n", "0.336013 | \n", "25.0 | \n", "2.0 | \n", "0.844997 | \n", "0.813290 | \n", "-0.457242 | \n", "0.178024 | \n", "0.057609 | \n", "2.003646 | \n", "
<xarray.Dataset> Size: 98MB\n", "Dimensions: (chain: 4, draw: 1000, date: 127, sweep: 12)\n", "Coordinates:\n", " * chain (chain) int64 32B 0 1 2 3\n", " * draw (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n", " * date (date) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " * sweep (sweep) float64 96B 0.0 0.1818 0.3636 ... 1.636 1.818 2.0\n", "Data variables:\n", " y (chain, draw, date, sweep) float64 49MB -0.4864 ... 0.2328\n", " marginal_effects (chain, draw, date, sweep) float64 49MB 0.8011 ... 0.1569\n", "Attributes:\n", " sweep_type: multiplicative\n", " var_names: ['influencer_spend']
<xarray.Dataset> Size: 33MB\n", "Dimensions: (chain: 4, draw: 1000, control: 2,\n", " fourier_mode: 4, date: 127,\n", " channel: 1)\n", "Coordinates:\n", " * chain (chain) int64 32B 0 1 2 3\n", " * draw (draw) int64 8kB 0 1 2 ... 998 999\n", " * control (control) <U18 144B 'shipping_th...\n", " * fourier_mode (fourier_mode) <U5 80B 'sin_1' ....\n", " * date (date) datetime64[ns] 1kB 2019-0...\n", " * channel (channel) <U16 64B 'influencer_s...\n", "Data variables:\n", " intercept_contribution (chain, draw) float64 32kB 0.554...\n", " adstock_alpha (chain, draw) float64 32kB 0.493...\n", " saturation_lam (chain, draw) float64 32kB 3.873...\n", " saturation_beta (chain, draw) float64 32kB 0.896...\n", " gamma_control (chain, draw, control) float64 64kB ...\n", " gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n", " y_sigma (chain, draw) float64 32kB 0.067...\n", " channel_contribution (chain, draw, date, channel) float64 4MB ...\n", " total_media_contribution_original_scale (chain, draw) float64 32kB 209.8...\n", " control_contribution (chain, draw, date, control) float64 8MB ...\n", " fourier_contribution (chain, draw, date, fourier_mode) float64 16MB ...\n", " yearly_seasonality_contribution (chain, draw, date) float64 4MB ...\n", "Attributes:\n", " created_at: 2025-07-29T14:43:35.144471+00:00\n", " arviz_version: 0.22.0\n", " inference_library: pymc\n", " inference_library_version: 5.25.1\n", " sampling_time: 19.752856969833374\n", " tuning_steps: 1000\n", " pymc_marketing_version: 0.15.1
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<xarray.Dataset> Size: 2kB\n", "Dimensions: (date: 127)\n", "Coordinates:\n", " * date (date) datetime64[ns] 1kB 2019-04-01 2019-04-08 ... 2021-08-30\n", "Data variables:\n", " y (date) float64 1kB 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n", "Attributes:\n", " created_at: 2025-07-29T14:43:35.820586+00:00\n", " arviz_version: 0.22.0\n", " inference_library: pymc\n", " inference_library_version: 5.25.1
<xarray.Dataset> Size: 6kB\n", "Dimensions: (channel: 1, date: 127, control: 2)\n", "Coordinates:\n", " * channel (channel) <U16 64B 'influencer_spend'\n", " * date (date) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " * control (control) <U18 144B 'shipping_threshold' 't'\n", "Data variables:\n", " channel_scale (channel) float64 8B 0.9919\n", " target_scale float64 8B 3.981\n", " channel_data (date, channel) float64 1kB 0.9189 0.2309 ... 0.2797 0.2041\n", " target_data (date) float64 1kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", " control_data (date, control) float64 2kB 25.0 0.0 25.0 ... 20.0 126.0\n", " dayofyear (date) int32 508B 91 98 105 112 119 ... 214 221 228 235 242\n", "Attributes:\n", " created_at: 2025-07-29T14:43:35.823810+00:00\n", " arviz_version: 0.22.0\n", " inference_library: pymc\n", " inference_library_version: 5.25.1
<xarray.Dataset> Size: 14kB\n", "Dimensions: (index: 127)\n", "Coordinates:\n", " * index (index) int64 1kB 0 1 2 3 4 5 ... 122 123 124 125 126\n", "Data variables: (12/14)\n", " date (index) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " year (index) int32 508B 2019 2019 2019 ... 2021 2021 2021\n", " month (index) int32 508B 4 4 4 4 4 5 5 5 5 ... 7 7 7 8 8 8 8 8\n", " dayofyear (index) int32 508B 91 98 105 112 119 ... 221 228 235 242\n", " t (index) int64 1kB 0 1 2 3 4 5 ... 122 123 124 125 126\n", " influencer_spend (index) float64 1kB 0.9189 0.2309 ... 0.2797 0.2041\n", " ... ...\n", " trend (index) float64 1kB 0.7783 0.7957 0.8126 ... 1.779 1.783\n", " cs (index) float64 1kB -0.01289 0.2258 ... -0.9747 -0.8932\n", " cc (index) float64 1kB 0.006446 -0.1136 ... -0.623 -0.5246\n", " seasonality (index) float64 1kB -0.003223 0.05608 ... -0.7089\n", " epsilon (index) float64 1kB -0.1188 0.06498 ... -0.3317 -0.05244\n", " y (index) float64 1kB 2.561 2.265 1.998 ... 2.734 2.607
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<xarray.Dataset> Size: 98MB\n", "Dimensions: (chain: 4, draw: 1000, date: 127, sweep: 12)\n", "Coordinates:\n", " * chain (chain) int64 32B 0 1 2 3\n", " * draw (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n", " * date (date) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " * sweep (sweep) float64 96B 0.0 0.1818 0.3636 ... 1.636 1.818 2.0\n", "Data variables:\n", " y (chain, draw, date, sweep) float64 49MB -0.4864 ... 0.2328\n", " marginal_effects (chain, draw, date, sweep) float64 49MB 0.8011 ... 0.1569\n", "Attributes:\n", " sweep_type: multiplicative\n", " var_names: ['influencer_spend']
<xarray.Dataset> Size: 98MB\n", "Dimensions: (chain: 4, draw: 1000, date: 127, sweep: 12)\n", "Coordinates:\n", " * chain (chain) int64 32B 0 1 2 3\n", " * draw (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n", " * date (date) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " * sweep (sweep) float64 96B 0.0 0.1818 0.3636 ... 1.636 1.818 2.0\n", "Data variables:\n", " y (chain, draw, date, sweep) float64 49MB -0.4861 ... 0.3638\n", " marginal_effects (chain, draw, date, sweep) float64 49MB 0.8691 ... -0.0...\n", "Attributes:\n", " sweep_type: absolute\n", " var_names: ['influencer_spend']
<xarray.Dataset> Size: 98MB\n", "Dimensions: (chain: 4, draw: 1000, date: 127, sweep: 12)\n", "Coordinates:\n", " * chain (chain) int64 32B 0 1 2 3\n", " * draw (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n", " * date (date) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " * sweep (sweep) float64 96B 0.0 0.1818 0.3636 ... 1.636 1.818 2.0\n", "Data variables:\n", " y (chain, draw, date, sweep) float64 49MB 0.1222 ... 0.365\n", " marginal_effects (chain, draw, date, sweep) float64 49MB 0.3145 ... -0.0...\n", "Attributes:\n", " sweep_type: additive\n", " var_names: ['influencer_spend']
<xarray.Dataset> Size: 98MB\n", "Dimensions: (chain: 4, draw: 1000, date: 127, sweep: 12)\n", "Coordinates:\n", " * chain (chain) int64 32B 0 1 2 3\n", " * draw (draw) int64 8kB 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n", " * date (date) datetime64[ns] 1kB 2019-04-01 ... 2021-08-30\n", " * sweep (sweep) float64 96B 0.0 0.09091 0.1818 ... 0.9091 1.0\n", "Data variables:\n", " y (chain, draw, date, sweep) float64 49MB 0.6685 ... 0.4112\n", " marginal_effects (chain, draw, date, sweep) float64 49MB -0.02118 ... -0...\n", "Attributes:\n", " sweep_type: absolute\n", " var_names: ['shipping_threshold']
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