feat(code): Add experiments for validity visualization
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load data, boilerplate:
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```{python}
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#| label: load-data
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#| echo: false
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from pathlib import Path
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import re
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@ -21,72 +22,27 @@ WORKING_DATA=DATA_DIR.joinpath("intermediate")
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PROCESSED_DATA=DATA_DIR.joinpath("processed")
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SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
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bib_string=""
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for partial_bib in RAW_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample_raw_db = bibtexparser.parse_string(bib_string)
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from src import prep_data
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bib_string=""
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for partial_bib in WORKING_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample = bibtexparser.parse_string(bib_string)
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```
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# raw database-search results
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bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
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# the complete library of sampled (and working) literature
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bib_sample = prep_data.bib_library_from_dir(WORKING_DATA)
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```{python}
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# load relevant studies
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from src import load_data
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# load zotero-based metadata: citations and uses
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zot_df = pd.DataFrame([
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[
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entry["doi"] if "doi" in entry.fields_dict else None,
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entry["times-cited"] if "times-cited" in entry.fields_dict else None,
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entry["usage"] if "usage" in entry.fields_dict else None,
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entry["keywords"] if "keywords" in entry.fields_dict else None,
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]
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for entry in bib_sample.entries
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], columns = ["doi", "cited", "usage", "keywords"]).drop_duplicates("doi").set_index("doi")
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# Add WB country grouping definitions (income group, world region)
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WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
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df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
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bib_df = (load_data.from_yml(f"{PROCESSED_DATA}")
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.assign(
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doi=lambda _df: _df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False),
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zot_cited=lambda _df: _df["doi"].map(zot_df["cited"]),
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zot_usage=lambda _df: _df["doi"].map(zot_df["usage"]),
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zot_keywords=lambda _df: _df["doi"].map(zot_df["keywords"]),
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date = lambda _df: pd.to_datetime(_df["year"], format="%Y"),
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year = lambda _df: _df["date"].dt.year,
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region = lambda _df: _df["country"].map(df_country_groups["Region"]),
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income_group = lambda _df: _df["country"].map(df_country_groups["Income group"]),
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)
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.query("year >= 2000")
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bib_df = prep_data.observations_with_metadata_df(
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raw_observations = load_data.from_yml(PROCESSED_DATA),
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study_metadata = prep_data.bib_metadata_df(bib_sample),
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country_groups = prep_data.country_groups_df(Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx")),
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)
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raw_observations = None
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zot_df = None
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df_country_groups = None
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```
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```{python}
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df_country_groups = pd.concat([pd.read_excel(WB_COUNTRY_GROUPS_FILE), pd.DataFrame(data={'Economy':['global'],'Code':['WLD'],'Region':['Europe & Central Asia;South Asia;North America;East Asia & Pacific;Sub-Saharan Africa;Europe & Central Asia;Latin America & Caribbean'], 'Income group':[''], 'Lending category':['']})]).set_index("Economy")
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def countries_to_regions(countries:str):
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res = set()
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for c in countries.replace(" ;", ";").replace("; ",";").split(";"):
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if c in df_country_groups.index:
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region = df_country_groups.at[c,'Region']
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res.add(region)
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return ";".join(res)
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# countries_to_regions("India; Nicaragua")
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bib_df['region'] = bib_df['country'].map(countries_to_regions)
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bib_df['region'].value_counts().plot.bar()
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```
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prep data:
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prep data:
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Map a 0-5 external validity score based on 'representativeness' to rows:
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@ -126,3 +82,81 @@ vd.loc[vd['method'].str.contains("|".join(["OLS","ordinary.least.square"])), 'va
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vd[['method', 'valid_int']]
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```
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## visualize data:
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Prep the by_intervention dataframe:
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```{python}
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#| label: fig-intervention-types
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#| fig-cap: Available studies by primary type of intervention
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by_intervention = (
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bib_df
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.fillna("")
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.groupby(["author", "year", "title", "design", "method", "region", "representativeness", "citation"])
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.agg(
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{
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"intervention": lambda _col: "; ".join(_col),
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}
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)
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.reset_index()
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.drop_duplicates()
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.assign(
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intervention=lambda _df: _df["intervention"].apply(
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lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
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),
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)
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.explode("intervention")
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)
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sort_order = by_intervention["intervention"].value_counts().index
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fig = plt.figure()
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fig.set_size_inches(6, 3)
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ax = sns.countplot(by_intervention, x="intervention", order=by_intervention["intervention"].value_counts().index)
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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rotation_mode="anchor")
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plt.show()
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```
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then visualize:
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validities as distplot with external as categorical x and internal as hue facet.
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Nicely shows that lower-internal generally have higher external and there are two external humps at 3 and 5
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(subnational and census-based)
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```{python}
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#| label: fig-validity-density
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from src.model import validity
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import seaborn.objects as so
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validities = validity.calculate(by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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# As distplot to show hue-facetted density
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sns.displot(
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data=validities,
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x="external_validity", hue="internal_validity",
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kind="kde",
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multiple="fill", clip=(0, None),
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palette="ch:rot=-0.5,hue=1.5,light=0.9",
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)
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```
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As a point-plot which shows the x and y correlation and the spread (roughly) per external validity
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```{python}
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#| label: fig-validity-points
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from src.model import validity
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import seaborn.objects as so
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validities = validity.calculate(by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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sns.pointplot(
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data=validities,
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x="internal_validity", y="external_validity"
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)
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```
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