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cetagostinicetagostini
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Correcting typo around yml files and csv files. (#1888)
1 parent 4631ba1 commit 670120a

13 files changed

+180
-15
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‎data/config_files/basic_model.yml

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- channel_1
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- channel_2
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# …
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target_column: "kpi_registrations"
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target_column: "y"
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# --- media transformations ---------------------------------------
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adstock:

‎data/config_files/example_with_original_scale_vars.yml

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- channel_1
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- channel_2
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# …
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target_column: "kpi_registrations"
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target_column: "y"
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# --- media transformations ---------------------------------------
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adstock:

‎data/config_files/multi_dimensional_hierarchical_model.yml

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- channel_1
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- channel_2
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# …
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target_column: "kpi_registrations"
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target_column: "y"
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dims: ["market"] # Keep as array for consistency
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# Standard scaling format
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scaling:

‎data/config_files/multi_dimensiona_hierarchical_model_nested_config.yml renamed to ‎data/config_files/multi_dimensional_hierarchical_model_nested_config.yml

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@@ -6,7 +6,7 @@ model:
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- channel_1
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- channel_2
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# …
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target_column: "kpi_registrations"
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target_column: "y"
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dims: ["market"] # Keep as array for consistency
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# Standard scaling format using class/kwargs pattern
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scaling:

‎data/config_files/multi_dimensional_hierarchical_with_arbitrary_effects_model.yml

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@@ -6,7 +6,7 @@ model:
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- channel_1
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- channel_2
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# …
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target_column: "kpi_registrations"
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target_column: "y"
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dims: ["market"] # Keep as array for consistency
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# Standard scaling format
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scaling:

‎data/config_files/multi_dimensional_model.yml

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@@ -6,7 +6,7 @@ model:
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- channel_1
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- channel_2
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# …
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target_column: "kpi_registrations"
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target_column: "y"
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dims: ["market"]
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# --- media transformations ---------------------------------------

‎data/config_files/multi_dimensional_with_events.yml

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model:
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class: pymc_marketing.mmm.multidimensional.MMM
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kwargs:
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date_column: "date"
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channel_columns:
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- channel_1
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- channel_2
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target_column: "y"
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dims: ["market"]
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scaling:
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target:
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method: "max"
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dims: []
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channel:
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method: "max"
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dims: []
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# --- media transformations ---------------------------------------
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adstock:
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class: pymc_marketing.mmm.GeometricAdstock
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kwargs:
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l_max: 24
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priors:
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alpha:
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distribution: "Beta"
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alpha: 1.0
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beta: 3.0
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dims: ["market", "channel"]
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saturation:
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class: pymc_marketing.mmm.MichaelisMentenSaturation
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kwargs:
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priors:
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lam:
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distribution: "Gamma"
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mu: 5.0
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sigma: 2.0
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dims: ["market"]
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alpha:
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distribution: "Beta"
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alpha: 1.0
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beta: 2.0
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dims: ["market", "channel"]
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# --- model (hierarchical) priors ---------------------------------
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model_config:
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intercept:
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distribution: Gamma
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mu: 0.5
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sigma: 1.0
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dims: ["market"]
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likelihood:
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distribution: TruncatedNormal
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lower: 0
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sigma:
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distribution: "Gamma"
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mu: 1.0
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sigma: 0.5
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dims: ["market"]
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dims: ["date", "market"]
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# ----------------------------------------------------------------------
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# (optional) sampler options you plan to forward to pm.sample():
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sampler_config:
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tune: 900
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draws: 500
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chains: 4
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random_seed: 42
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target_accept: 0.92
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# nuts_sampler: "nutpie"
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# nuts_sampler_kwargs:
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# backend: "jax"
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# ----------------------------------------------------------------------
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# Effects with complex priors
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effects:
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# 1. Linear Trend Effect with complex nested priors
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- class: pymc_marketing.mmm.additive_effect.LinearTrendEffect
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kwargs:
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trend:
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class: pymc_marketing.mmm.LinearTrend
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kwargs:
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n_changepoints: 8
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include_intercept: false
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priors:
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delta:
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distribution: "Laplace"
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mu: 0
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b: 0.2
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dims: ["market", "changepoint"]
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dims: ["market"]
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prefix: "trend"
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# 2. Fourier Effect with complex nested priors
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- class: pymc_marketing.mmm.additive_effect.FourierEffect
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kwargs:
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fourier:
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class: pymc_marketing.mmm.WeeklyFourier
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kwargs:
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n_order: 3
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prefix: "weekly_fourier"
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prior:
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distribution: "Laplace"
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mu: 0
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b: 0.1
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dims: ["weekly_fourier", "market"]
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# 3. Events upper funnel
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- class: pymc_marketing.mmm.additive_effect.EventAdditiveEffect
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kwargs:
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effect:
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class: pymc_marketing.mmm.events.EventEffect
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kwargs:
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basis:
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class: pymc_marketing.mmm.events.GaussianBasis
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kwargs:
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priors:
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sigma:
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distribution: "Gamma"
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mu: 5.0
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sigma: 2.0
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dims: ["event"]
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effect_size:
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class: pymc_extras.prior.Prior
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kwargs:
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distribution: "Beta"
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alpha: 1
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beta: 2
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dims: ["event"]
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dims: ["event"]
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df_events:
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class: pandas.DataFrame
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kwargs:
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data: {
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"name": ["flight 1", "flight 2", "flight 3", "flight 4"],
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"market": ["US", "US", "US", "US"],
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"start_date": [
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"2023-01-10",
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"2023-01-20",
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"2023-02-01",
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"2023-02-20",
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],
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"end_date": [
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"2023-01-15",
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"2023-01-25",
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"2023-02-05",
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"2023-03-15",
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],
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}
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prefix: "event"
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original_scale_vars:
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- y
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- channel_contribution
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- intercept_contribution
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- trend_effect_contribution
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# ----------------------------------------------------------------------
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# (optional) idata from a previous sample
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# idata_path: "data/idata.nc"
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# ----------------------------------------------------------------------
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# (optional) Data paths
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# data:
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# X_path: "data/X.csv"
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# y_path: "data/y.csv"

‎data/processed/y.csv

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kpi_registrations
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y
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45.45380610164261
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42.51634583133759
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54.25093939558018

‎docs/source/notebooks/mmm/mmm_build_from_yml_example.ipynb

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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>kpi_registrations</th>\n",
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" <th>y</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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"</div>"
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],
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"text/plain": [
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" kpi_registrations\n",
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" y\n",
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"0 45.453806\n",
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"1 42.516346\n",
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"2 54.250939"
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"source": [
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"mmm.fit(\n",
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" X=X,\n",
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" y=y.kpi_registrations,\n",
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" y=y.y,\n",
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" random_seed=42,\n",
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")\n",
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"\n",
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" - channel_1\n",
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" - channel_2\n",
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" # …\n",
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" target_column: \"kpi_registrations\"\n",
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" target_column: \"y\"\n",
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"\n",
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" # --- media transformations ---------------------------------------\n",
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" adstock:\n",

‎tests/mmm/builders/config_files/wrong_adstock_class.yml

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channel_columns:
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target_column: "kpi_registrations"
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target_column: "y"
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# Wrong adstock configuration - invalid class name
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adstock:

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