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datasets_labeled.py
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datasets_labeled.py
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import pandas as pd
from datetime import timedelta
from src.ETL.dataset import LabeledDataset
from src.ETL.processor import Processor
from src.ETL.constants import LON, LAT
def clean_pv_kenya(df: pd.DataFrame) -> pd.DataFrame:
df = df[(df["harvest_da"] != "nan") & (df["harvest_da"] != "unknown")].copy()
df.loc[:, "planting_d"] = pd.to_datetime(df["planting_d"])
df.loc[:, "harvest_da"] = pd.to_datetime(df["harvest_da"])
df["between_days"] = (df["harvest_da"] - df["planting_d"]).dt.days
year = pd.to_timedelta(timedelta(days=365))
df.loc[(-365 < df["between_days"]) & (df["between_days"] < 0), "harvest_da"] += year
valid_years = [2018, 2019, 2020]
df = df[
(df["planting_d"].dt.year.isin(valid_years)) & (df["harvest_da"].dt.year.isin(valid_years))
].copy()
df = df[(0 < df["between_days"]) & (df["between_days"] <= 365)]
return df
def clean_geowiki(df: pd.DataFrame) -> pd.DataFrame:
df = df.groupby("location_id").mean()
df = df.rename(
{"sumcrop": "mean_sumcrop"},
axis="columns",
errors="raise",
)
return df.reset_index()
def clean_one_acre_fund(df: pd.DataFrame) -> pd.DataFrame:
df = df[
df[LON].notnull()
& df[LAT].notnull()
& df["harvesting_date"].notnull()
& df["planting_date"].notnull()
].copy()
return df
def clean_ceo_data(df: pd.DataFrame) -> pd.DataFrame:
df = df[df[LON].notnull() & df[LAT].notnull() & (df["flagged"] == False)].copy() # noqa E712
# CEO data may have duplicate samples labeled by the same person
df = df[~df[[LAT, LON, "email"]].duplicated(keep="first")]
return df
labeled_datasets = [
LabeledDataset(
dataset="geowiki_landcover_2017",
country="global",
processors=(
Processor(
filename="loc_all_2.txt",
clean_df=clean_geowiki,
longitude_col="loc_cent_X",
latitude_col="loc_cent_Y",
crop_prob=lambda df: df.mean_sumcrop / 100,
start_year=2017,
x_y_from_centroid=False,
),
),
),
LabeledDataset(
dataset="Kenya",
country="Kenya",
processors=(
Processor(
filename="noncrop_labels_v2",
crop_prob=0.0,
start_year=2019,
train_val_test=(0.8, 0.1, 0.1),
transform_crs_from=32636,
),
Processor(
filename="noncrop_labels_set2",
crop_prob=0.0,
start_year=2019,
train_val_test=(0.8, 0.1, 0.1),
transform_crs_from=32636,
),
Processor(
filename="2019_gepro_noncrop",
crop_prob=0.0,
start_year=2019,
train_val_test=(0.8, 0.1, 0.1),
),
Processor(
filename="noncrop_water_kenya_gt",
crop_prob=0.0,
start_year=2019,
train_val_test=(0.8, 0.1, 0.1),
),
Processor(
filename="noncrop_kenya_gt",
crop_prob=0.0,
start_year=2019,
train_val_test=(0.8, 0.1, 0.1),
),
Processor(
filename="one_acre_fund_kenya",
crop_prob=1.0,
start_year=2019,
longitude_col="Lon",
latitude_col="Lat",
train_val_test=(0.8, 0.1, 0.1),
x_y_from_centroid=False,
),
Processor(
filename="plant_village_kenya",
clean_df=clean_pv_kenya,
crop_prob=1.0,
plant_date_col="planting_d",
train_val_test=(0.8, 0.1, 0.1),
transform_crs_from=32636,
),
)
+ tuple(
[
Processor(
filename=f"ref_african_crops_kenya_01_labels_0{i}/labels.geojson",
longitude_col="Longitude",
latitude_col="Latitude",
crop_prob=1.0,
plant_date_col="Planting Date",
train_val_test=(0.8, 0.1, 0.1),
transform_crs_from=32636,
)
for i in [0, 1, 2]
]
),
),
LabeledDataset(
dataset="Mali",
country="Mali",
processors=(
Processor(filename="mali_noncrop_2019", crop_prob=0.0, start_year=2019),
Processor(filename="segou_bounds_07212020", crop_prob=1.0, start_year=2018),
Processor(filename="segou_bounds_07212020", crop_prob=1.0, start_year=2019),
Processor(filename="sikasso_clean_fields", crop_prob=1.0, start_year=2019),
),
),
LabeledDataset(
dataset="Mali_lower_CEO_2019",
country="Mali",
processors=(
Processor(
filename="ceo-2019-Mali-USAID-ZOIS-lower-(Set-1)--sample-data-2021-11-29.csv",
crop_prob=lambda df: (
df["Does this point lie on a crop or non-crop pixel?"] == "Crop"
),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
),
Processor(
filename="ceo-2019-Mali-USAID-ZOIS-lower-(Set-2)--sample-data-2021-11-29.csv",
crop_prob=lambda df: (
df["Does this point lie on a crop or non-crop pixel?"] == "Crop"
),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
),
),
),
LabeledDataset(
dataset="Mali_upper_CEO_2019",
country="Mali",
processors=(
Processor(
filename="ceo-2019-Mali-USAID-ZOIS-upper-(Set-1)-sample-data-2021-11-25.csv",
crop_prob=lambda df: (
df["Does this point lie on a crop or non-crop pixel?"] == "Crop"
),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
),
Processor(
filename="ceo-2019-Mali-USAID-ZOIS-upper-(Set-2)-sample-data-2021-11-25.csv",
crop_prob=lambda df: (
df["Does this point lie on a crop or non-crop pixel?"] == "Crop"
),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
),
),
),
LabeledDataset(
dataset="Togo",
country="Togo",
processors=(
Processor(
filename="crop_merged_v2",
crop_prob=1.0,
train_val_test=(0.8, 0.2, 0.0),
start_year=2019,
),
Processor(
filename="noncrop_merged_v2",
crop_prob=0.0,
train_val_test=(0.8, 0.2, 0.0),
start_year=2019,
),
Processor(
filename="random_sample_hrk",
crop_prob=lambda df: df["hrk-label"],
transform_crs_from=32631,
train_val_test=(0.0, 0.0, 1.0),
start_year=2019,
),
Processor(
filename="random_sample_cn",
crop_prob=lambda df: df["cn_labels"],
train_val_test=(0.0, 0.0, 1.0),
start_year=2019,
),
Processor(
filename="BB_random_sample_1k",
crop_prob=lambda df: df["bb_label"],
train_val_test=(0.0, 0.0, 1.0),
start_year=2019,
),
Processor(
filename="random_sample_bm",
crop_prob=lambda df: df["bm_labels"],
train_val_test=(0.0, 0.0, 1.0),
start_year=2019,
),
),
),
LabeledDataset(
dataset="Rwanda",
country="Rwanda",
processors=tuple(
[
Processor(
filename=filename,
crop_prob=lambda df: df["Crop/ or not"] == "Cropland",
x_y_from_centroid=False,
train_val_test=(
0.1,
0.45,
0.45,
), # this makes about 525 for validation and test
start_year=2019,
)
for filename in [
"ceo-2019-Rwanda-Cropland-(RCMRD-Set-1)-sample-data-2021-04-20.csv",
"ceo-2019-Rwanda-Cropland-(RCMRD-Set-2)-sample-data-2021-04-20.csv",
"ceo-2019-Rwanda-Cropland-sample-data-2021-04-20.csv",
]
]
+ [
Processor(filename="Rwanda-non-crop-corrective-v1", crop_prob=0.0, start_year=2019),
Processor(filename="Rwanda-crop-corrective-v1", crop_prob=1.0, start_year=2019),
]
),
),
LabeledDataset(
dataset="Uganda",
country="Uganda",
processors=tuple(
[
Processor(
filename=filename,
crop_prob=lambda df: df["Crop/non-crop"] == "Cropland",
x_y_from_centroid=False,
train_val_test=(
0.10,
0.45,
0.45,
), # this makes about 525 for validation and test
start_year=2019,
)
for filename in [
"ceo-2019-Uganda-Cropland-(RCMRD--Set-1)-sample-data-2021-06-11.csv",
"ceo-2019-Uganda-Cropland-(RCMRD--Set-2)-sample-data-2021-06-11.csv",
]
]
+ [
Processor(
filename=filename,
crop_prob=0.0,
sample_from_polygon=True,
x_y_from_centroid=True,
start_year=2020,
)
for filename in [
"WDPA_WDOECM_Aug2021_Public_UGA_shp_0.zip",
"WDPA_WDOECM_Aug2021_Public_UGA_shp_1.zip",
"WDPA_WDOECM_Aug2021_Public_UGA_shp_2.zip",
]
]
+ [
Processor(
filename="ug_in_season_monitoring_2021_08_11_17_50_46_428737.csv",
crop_prob=1.0,
longitude_col="location/_gps_longitude",
latitude_col="location/_gps_latitude",
plant_date_col="start",
x_y_from_centroid=False,
),
Processor(
filename="ug_end_of_season_assessment_2021_08_11_17_47_53_813908.csv",
crop_prob=1.0,
longitude_col="district_selection/_gps_location_longitude",
latitude_col="district_selection/_gps_location_latitude",
plant_date_col="start",
x_y_from_centroid=False,
),
Processor(
filename="ug_pre_season_assessment_2021_08_11_18_15_27_323695.csv",
crop_prob=1.0,
longitude_col="location/_gps_location_longitude",
latitude_col="location/_gps_location_latitude",
plant_date_col="start",
x_y_from_centroid=False,
),
]
),
),
# LabeledDataset(
# dataset="one_acre_fund",
# country="Kenya,Rwanda,Tanzania",
# processors=(
# Processor(
# filename="One_Acre_Fund_KE_RW_TZ_2016_17_18_19_MEL_agronomic_survey_data.csv",
# crop_prob=1.0,
# clean_df=clean_one_acre_fund,
# longitude_col="site_longitude",
# latitude_col="site_latitude",
# plant_date_col="planting_date",
# x_y_from_centroid=False,
# ),
# ),
# ),
LabeledDataset(
dataset="open_buildings",
country="global",
processors=(
Processor(
filename="177_buildings_confidence_0.9.csv",
latitude_col="latitude",
longitude_col="longitude",
crop_prob=0.0,
start_year=2020,
x_y_from_centroid=False,
),
),
),
LabeledDataset(
dataset="digitalearthafrica_eastern",
country="global",
processors=(
Processor(
filename="Eastern_training_data_20210427.geojson",
crop_prob=lambda df: df["Class"].astype(float),
start_year=2020,
),
),
),
LabeledDataset(
dataset="digitalearthafrica_sahel",
country="global",
processors=(
Processor(
filename="ceo_td_polys.geojson",
crop_prob=lambda df: df["Class"].astype(float),
start_year=2019,
),
Processor(
filename="validation_samples.shp",
crop_prob=lambda df: (df["Class"] == "crop").astype(float),
start_year=2019,
),
Processor(
filename="Sahel_region_RE_sample.geojson",
crop_prob=lambda df: (df["Class"] == "crop").astype(float),
start_year=2019,
),
),
),
LabeledDataset(
dataset="Ethiopia",
country="Ethiopia",
processors=(
Processor(filename="tigray/tigrayWW_crop.shp", crop_prob=1.0, start_year=2019),
Processor(filename="tigray/tigrayWW_crop2.shp", crop_prob=1.0, start_year=2019),
Processor(filename="tigray/tigrayWW_forest.shp", crop_prob=0.0, start_year=2019),
Processor(filename="tigray/tigrayWW_forest2.shp", crop_prob=0.0, start_year=2019),
Processor(filename="tigray/tigrayWW_shrub.shp", crop_prob=0.0, start_year=2019),
Processor(filename="tigray/tigrayWW_shrub2.shp", crop_prob=0.0, start_year=2019),
Processor(filename="tigray/tigrayWW_sparse.shp", crop_prob=0.0, start_year=2019),
Processor(filename="tigray/tigrayWW_sparse2.shp", crop_prob=0.0, start_year=2019),
Processor(
filename="tigray_non_fallow_crop/nonFallowCrop2019.shp",
crop_prob=1.0,
start_year=2019,
),
Processor(
filename="tigray_non_fallow_crop/nonFallowCrop2020.shp",
crop_prob=1.0,
start_year=2020,
),
Processor(
filename="tigray_corrective_2020/non_crop.shp", crop_prob=0.0, start_year=2020
),
Processor(filename="tigray_corrective_2020/crop.shp", crop_prob=1.0, start_year=2020),
Processor(
filename="tigray_corrective_2021/non_crop.shp",
crop_prob=0.0,
start_year=2021,
),
Processor(
filename="tigray_corrective_2021/crop.shp",
crop_prob=1.0,
start_year=2021,
),
),
),
LabeledDataset(
dataset="Ethiopia_Tigray_2020",
country="Ethiopia",
processors=(
Processor(
filename="ceo-2020-Ethiopia-Tigray-(Set-1)-sample-data-2021-10-04.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2020,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
label_names="email",
label_dur="analysis_duration",
),
Processor(
filename="ceo-2020-Ethiopia-Tigray-(Set-2)-sample-data-2021-10-04.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2020,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
label_names="email",
label_dur="analysis_duration",
),
),
),
LabeledDataset(
dataset="Ethiopia_Tigray_2021",
country="Ethiopia",
processors=(
Processor(
filename="ceo-2021-Ethiopia-Tigray-(Set-1-Fixed)-sample-data-2022-02-24.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2021,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
label_names="email",
label_dur="analysis_duration",
),
Processor(
filename="ceo-2021-Ethiopia-Tigray-(Set-2-Fixed)-sample-data-2022-02-24.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2021,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
label_names="email",
label_dur="analysis_duration",
),
),
),
LabeledDataset(
dataset="Ethiopia_Bure_Jimma_2019",
country="Ethiopia",
processors=(
Processor(
filename="ceo-2019-Ethiopia---Bure-Jimma-(Set-1)-sample-data-2021-11-24.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
),
Processor(
filename="ceo-2019-Ethiopia---Bure-Jimma-(Set-2)-sample-data-2021-11-24.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
),
),
),
LabeledDataset(
dataset="Ethiopia_Bure_Jimma_2020",
country="Ethiopia",
processors=(
Processor(
filename="ceo-2020-Ethiopia---Bure-Jimma-(Set-1)-sample-data-2021-11-24.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
),
Processor(
filename="ceo-2020-Ethiopia---Bure-Jimma-(Set-2)-sample-data-2021-11-24.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
),
),
),
LabeledDataset(
dataset="Argentina_Buenos_Aires",
country="Argentina",
processors=(
Processor(
filename="bc_mapeo_del_cultivo_0.csv",
clean_df=lambda df: df[
(
df["Seleccione el cultivo principal en el lote:"].notnull()
& ~df["Seleccione el cultivo principal en el lote:"].isin(
["otro", "barbecho", "sin_dato"]
)
)
].copy(),
longitude_col="longitud",
latitude_col="latitud",
crop_type_col="Seleccione el cultivo principal en el lote:",
crop_prob=lambda df: df["Seleccione el cultivo principal en el lote:"].isin(
["trigo_o_cebada", "cultive_leguminosa", "maiz", "sorgo", "soja", "girasol"]
),
x_y_from_centroid=False,
train_val_test=(0.8, 0.2, 0.0),
start_year=2021,
),
),
),
LabeledDataset(
dataset="Malawi_CEO_2020",
country="Malawi",
processors=(
Processor(
filename="ceo-Sep-2020-Sep-2021-Malawi-(Set-1)-sample-data-2021-12-09.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2020,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
),
Processor(
filename="ceo-Sep-2020-Sep-2021-Malawi-(Set-2)-sample-data-2021-12-09.csv",
crop_prob=lambda df: (df["Does this pixel contain active cropland?"] == "Crop"),
start_year=2020,
x_y_from_centroid=False,
train_val_test=(0.0, 0.5, 0.5),
clean_df=clean_ceo_data,
),
),
),
LabeledDataset(
dataset="Malawi_CEO_2019",
country="Malawi",
processors=(
Processor(
filename="ceo-2019-Malawi-Cropland-(RCMRD-Set-1)-sample-data-2021-12-10.csv",
crop_prob=lambda df: (df["Crop/non"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
clean_df=clean_ceo_data,
),
Processor(
filename="ceo-2019-Malawi-Cropland-(RCMRD-Set-2)-sample-data-2021-12-10.csv",
crop_prob=lambda df: (df["Crop/non"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
clean_df=clean_ceo_data,
),
),
),
LabeledDataset(
dataset="Malawi_FAO",
country="Malawi",
processors=(Processor(filename="malawi_fao.geojson", crop_prob=1.0, start_year=2020),),
),
LabeledDataset(
dataset="Malawi_FAO_corrected",
country="Malawi",
processors=(
Processor(filename="MWI_MWI_LC_FO_202106.shp", crop_prob=1.0, start_year=2020),
),
),
LabeledDataset(
dataset="Zambia_CEO_2019",
country="Zambia",
processors=(
Processor(
filename="ceo-2019-Zambia-Cropland-(RCMRD-Set-1)-sample-data-2021-12-12.csv",
crop_prob=lambda df: (df["Crop/non-crop"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
clean_df=clean_ceo_data,
),
Processor(
filename="ceo-2019-Zambia-Cropland-(RCMRD-Set-2)-sample-data-2021-12-12.csv",
crop_prob=lambda df: (df["Crop/non-crop"] == "Crop"),
start_year=2019,
x_y_from_centroid=False,
clean_df=clean_ceo_data,
),
),
),
LabeledDataset(
dataset="Tanzania_CEO_2019",
country="Tanzania",
processors=(
Processor(
filename="ceo-2019-Tanzania-Cropland-(RCMRD-Set-1)-sample-data-2021-12-13.csv",
crop_prob=lambda df: (df["Crop/non-Crop"] == "Cropland"),
start_year=2019,
train_val_test=(0.0, 0.5, 0.5),
x_y_from_centroid=False,
clean_df=clean_ceo_data,
),
Processor(
filename="ceo-2019-Tanzania-Cropland-(RCMRD-Set-2)-sample-data-2021-12-13.csv",
crop_prob=lambda df: (df["Crop/non-Crop"] == "Cropland"),
start_year=2019,
train_val_test=(0.0, 0.5, 0.5),
x_y_from_centroid=False,
clean_df=clean_ceo_data,
),
),
),
LabeledDataset(
dataset="Malawi_corrected",
country="Malawi",
processors=(
Processor(
filename="Crops.shp",
crop_prob=1.0,
start_year=2020,
),
Processor(
filename="Noncrops.shp",
crop_prob=0.0,
start_year=2020,
),
Processor(
filename="Major_protected_areas.shp",
crop_prob=0.0,
start_year=2020,
),
)
)
]