File size: 3,725 Bytes
68e6513 fbdc657 144f299 fbdc657 68e6513 144f299 68e6513 1580227 68e6513 fbdc657 ee6a180 3433b65 144f299 ee6a180 144f299 1580227 ee6a180 144f299 ee6a180 144f299 ee6a180 144f299 ee6a180 3433b65 144f299 3433b65 144f299 3433b65 144f299 3433b65 ee6a180 3433b65 144f299 7ce5480 fbdc657 3433b65 144f299 fbdc657 3433b65 144f299 fbdc657 3433b65 144f299 68e6513 144f299 ee6a180 68e6513 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# Utility functions for filtering the dataframe
import pandas as pd
import assets.text_content as tc
def filter_cols(df):
df = df[[
tc.MODEL_NAME,
tc.CLEMSCORE,
tc.INPUT,
tc.OUTPUT,
tc.LATENCY,
tc.CONTEXT,
tc.PARAMS,
tc.RELEASE_DATE,
tc.LICENSE
]]
return df
def filter(df, language_list, parameters, input_price, output_price, multimodal,
context, open_weight, start, end, license ):
if not df.empty: # Check if df is non-empty
df = df[df[tc.LANGS].apply(lambda x: all(lang in x for lang in language_list))]
if not df.empty:
# Split dataframe by Open Weight
open_weight_true = df[df[tc.OPEN_WEIGHT] == True]
open_weight_false = df[df[tc.OPEN_WEIGHT] == False]
# Get max parameter size for open weight models
max_parameter_size = open_weight_true[tc.PARAMS].max() if not open_weight_true.empty else 0
# Filter only the open weight models based on parameters
if not open_weight_true.empty:
if parameters[1] >= max_parameter_size:
filtered_open = open_weight_true[
(open_weight_true[tc.PARAMS] >= parameters[0])
]
else:
filtered_open = open_weight_true[
(open_weight_true[tc.PARAMS] >= parameters[0]) &
(open_weight_true[tc.PARAMS] <= parameters[1])
]
# Combine filtered open weight models with unfiltered commercial models
df = pd.concat([filtered_open, open_weight_false])
if not df.empty: # Check if df is non-empty
df = df[(df[tc.INPUT] >= input_price[0]) & (df[tc.INPUT] <= input_price[1])]
if not df.empty: # Check if df is non-empty
df = df[(df[tc.OUTPUT] >= output_price[0]) & (df[tc.OUTPUT] <= output_price[1])]
if not df.empty: # Check if df is non-empty
if tc.SINGLE_IMG in multimodal:
df = df[df[tc.SINGLE_IMG] == True]
if tc.MULT_IMG in multimodal:
df = df[df[tc.MULT_IMG] == True]
if tc.AUDIO in multimodal:
df = df[df[tc.AUDIO] == True]
if tc.VIDEO in multimodal:
df = df[df[tc.VIDEO] == True]
# if not df.empty: # Check if df is non-empty
# df = df[(df['Context Size (k)'] >= (context[0])) & (df['Context Size (k)'] <= (context[1]))]
if not df.empty: # Check if df is non-empty
if tc.OPEN in open_weight and tc.COMM not in open_weight:
df = df[df[tc.OPEN_WEIGHT] == True]
elif tc.COMM in open_weight and tc.OPEN not in open_weight:
df = df[df[tc.OPEN_WEIGHT] == False]
elif tc.OPEN not in open_weight and tc.COMM not in open_weight:
# Return empty DataFrame with same columns
df = pd.DataFrame(columns=df.columns)
if not df.empty: # Check if df is non-empty
df = df[df[tc.LICENSE_NAME].apply(lambda x: any(lic in x for lic in license))]
# Convert 'Release Date' to int temporarily
if not df.empty: # Check if df is non-empty
df[tc.TEMP_DATE] = pd.to_datetime(df[tc.TEMP_DATE]).astype(int) // 10**9 # Convert to seconds since epoch
# Convert start and end to int (seconds since epoch)
start = int(pd.to_datetime(start).timestamp())
end = int(pd.to_datetime(end).timestamp())
# Filter based on the converted 'Release Date'
if not df.empty: # Check if df is non-empty
df = df[(df[tc.TEMP_DATE] >= start) & (df[tc.TEMP_DATE] <= end)]
df = filter_cols(df)
df = df.sort_values(by=tc.CLEMSCORE, ascending=False)
return df # Return the filtered dataframe
|